Journal articles: 'Voice identification'

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Sun, YuXiang, Lili Ming, Jiamin Sun, FeiFei Guo, Qiufeng Li, and Xueping Hu. "Brain mechanism of unfamiliar and familiar voice processing: an activation likelihood estimation meta-analysis." PeerJ 11 (March 13, 2023): e14976. http://dx.doi.org/10.7717/peerj.14976.

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Interpersonal communication through vocal information is very important for human society. During verbal interactions, our vocal cord vibrations convey important information regarding voice identity, which allows us to decide how to respond to speakers (e.g., neither greeting a stranger too warmly or speaking too coldly to a friend). Numerous neural studies have shown that identifying familiar and unfamiliar voices may rely on different neural bases. However, the mechanism underlying voice identification of individuals of varying familiarity has not been determined due to vague definitions, confusion of terms, and differences in task design. To address this issue, the present study first categorized three kinds of voice identity processing (perception, recognition and identification) from speakers with different degrees of familiarity. We defined voice identity perception as passively listening to a voice or determining if the voice was human, voice identity recognition as determining if the sound heard was acoustically familiar, and voice identity identification as ascertaining whether a voice is associated with a name or face. Of these, voice identity perception involves processing unfamiliar voices, and voice identity recognition and identification involves processing familiar voices. According to these three definitions, we performed activation likelihood estimation (ALE) on 32 studies and revealed different brain mechanisms underlying processing of unfamiliar and familiar voice identities. The results were as follows: (1) familiar voice recognition/identification was supported by a network involving most regions in the temporal lobe, some regions in the frontal lobe, subcortical structures and regions around the marginal lobes; (2) the bilateral superior temporal gyrus was recruited for voice identity perception of an unfamiliar voice; (3) voice identity recognition/identification of familiar voices was more likely to activate the right frontal lobe than voice identity perception of unfamiliar voices, while voice identity perception of an unfamiliar voice was more likely to activate the bilateral temporal lobe and left frontal lobe; and (4) the bilateral superior temporal gyrus served as a shared neural basis of unfamiliar voice identity perception and familiar voice identity recognition/identification. In general, the results of the current study address gaps in the literature, provide clear definitions of concepts, and indicate brain mechanisms for subsequent investigations.

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Hammarstrom, C. "Voice Identification." Australian Journal of Forensic Sciences 19, no. 3 (March 1987): 95–99. http://dx.doi.org/10.1080/00450618709410271.

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Plante-Hébert, Julien, Victor J. Boucher, and Boutheina Jemel. "The processing of intimately familiar and unfamiliar voices: Specific neural responses of speaker recognition and identification." PLOS ONE 16, no. 4 (April 16, 2021): e0250214. http://dx.doi.org/10.1371/journal.pone.0250214.

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Research has repeatedly shown that familiar and unfamiliar voices elicit different neural responses. But it has also been suggested that different neural correlates associate with the feeling of having heard a voice and knowing who the voice represents. The terminology used to designate these varying responses remains vague, creating a degree of confusion in the literature. Additionally, terms serving to designate tasks of voice discrimination, voice recognition, and speaker identification are often inconsistent creating further ambiguities. The present study used event-related potentials (ERPs) to clarify the difference between responses to 1) unknown voices, 2) trained-to-familiar voices as speech stimuli are repeatedly presented, and 3) intimately familiar voices. In an experiment, 13 participants listened to repeated utterances recorded from 12 speakers. Only one of the 12 voices was intimately familiar to a participant, whereas the remaining 11 voices were unfamiliar. The frequency of presentation of these 11 unfamiliar voices varied with only one being frequently presented (the trained-to-familiar voice). ERP analyses revealed different responses for intimately familiar and unfamiliar voices in two distinct time windows (P2 between 200–250 ms and a late positive component, LPC, between 450–850 ms post-onset) with late responses occurring only for intimately familiar voices. The LPC present sustained shifts, and short-time ERP components appear to reflect an early recognition stage. The trained voice equally elicited distinct responses, compared to rarely heard voices, but these occurred in a third time window (N250 between 300–350 ms post-onset). Overall, the timing of responses suggests that the processing of intimately familiar voices operates in two distinct steps of voice recognition, marked by a P2 on right centro-frontal sites, and speaker identification marked by an LPC component. The recognition of frequently heard voices entails an independent recognition process marked by a differential N250. Based on the present results and previous observations, it is proposed that there is a need to distinguish between processes of voice “recognition” and “identification”. The present study also specifies test conditions serving to reveal this distinction in neural responses, one of which bears on the length of speech stimuli given the late responses associated with voice identification.

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McGorrery, Paul Gordon, and Marilyn McMahon. "A fair ‘hearing’." International Journal of Evidence & Proof 21, no. 3 (February 17, 2017): 262–86. http://dx.doi.org/10.1177/1365712717690753.

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Voice identification evidence, identifying an offender by the sound of their voice, is sometimes the only means of identifying someone who has committed a crime. Auditory memory is, however, associated with poorer performance than visual memory, and is subject to distinctive sources of unreliability. Consequently, it is important for investigating authorities to adopt appropriate strategies when dealing with voice identification, particularly when the identification involves a voice previously unknown to the witness. Appropriate voice identification parades conducted by police can offer an otherwise unavailable means of identifying the offender. This article suggests some ‘best practice’ techniques for voice identification parades and then, using reported Australian criminal cases as case studies, evaluates voice identification parade procedures used by police. Overall, we argue that the case studies reveal practices that are inconsistent with current scientific understandings about auditory memory and voice identifications, and that courts are insufficiently attending to the problems associated with this evidence.

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Adhyke, Yuzy Prila, Anis Eliyana, Ahmad Rizki Sridadi, Dina Fitriasia Septiarini, and Aisha Anwar. "Hear Me Out! This Is My Idea: Transformational Leadership, Proactive Personality and Relational Identification." SAGE Open 13, no. 1 (January 2023): 215824402211458. http://dx.doi.org/10.1177/21582440221145869.

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This study proposes that there is relationship between transformational leadership and employee’s voice as well as relational identification as a mediation and proactive personality as a moderator. Structural Equation Modeling was used to analyze data gathered from employees at the Ministry of Law and Human Rights through questionnaires. The findings revealed that transformational leadership has a significant effect on employee’s voice and relational identification; relational identification mediates the relation between transformational leadership and employee voice behavior, and proactive personality will weaken the transformational effect on employee’s voice behavior. This study enriches empirical studies that employee’s voice can represent the opinions and ideas of employees with the presence of relational identification, proactive personality, and transformational leadership in the organization. Furthermore, transformational leadership can build relational identification that is strengthened by a proactive personality so that employees are happy to convey their voices.

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Liang, Tsang-Lang, Hsueh-Feng Chang, Ming-Hsiang Ko, and Chih-Wei Lin. "Transformational leadership and employee voices in the hospitality industry." International Journal of Contemporary Hospitality Management 29, no. 1 (January 9, 2017): 374–92. http://dx.doi.org/10.1108/ijchm-07-2015-0364.

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Purpose This study aims to explore the relationship between transformational leadership and employee voice behavior and the role of relational identification and work engagement as mediators in the same. Design/methodology/approach This study uses structural equation modeling to analyze the data from a questionnaire survey of 251 Taiwanese hospitality industry employees. Findings The findings demonstrate that transformational leadership has significant relationships with relational identification, work engagement and employee voice behavior and that relational identification and work engagement sequentially mediate between transformational leadership and employee voice behavior. Practical implications The results of this study provide insights into the intervening mechanisms linking leaders’ behavior with employees’ voices, while also highlighting the potential importance of relational identification in organizations, especially concerning the enhancement of employees’ work engagement and voice. Originality/value The findings reveal the mechanisms by which supervisors’ transformational leadership encourages employees to voice their suggestions, providing empirical evidence of the sequential mediation of relational identification and work engagement. The results help clarify the psychological process by which leaders influence their followers.

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Mohamed, Amira A., Amira Eltokhy, and Abdelhalim A. Zekry. "Enhanced Multiple Speakers’ Separation and Identification for VOIP Applications Using Deep Learning." Applied Sciences 13, no. 7 (March 28, 2023): 4261. http://dx.doi.org/10.3390/app13074261.

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Institutions have been adopting work/study-from-home programs since the pandemic began. They primarily utilise Voice over Internet Protocol (VoIP) software to perform online meetings. This research introduces a new method to enhance VoIP calls experience using deep learning. In this paper, integration between two existing techniques, Speaker Separation and Speaker Identification (SSI), is performed using deep learning methods with effective results as introduced by state-of-the-art research. This integration is applied to VoIP system application. The voice signal is introduced to the speaker separation and identification system to be separated; then, the “main speaker voice” is identified and verified rather than any other human or non-human voices around the main speaker. Then, only this main speaker voice is sent over IP to continue the call process. Currently, the online call system depends on noise cancellation and call quality enhancement. However, this does not address multiple human voices over the call. Filters used in the call process only remove the noise and the interference (de-noising speech) from the speech signal. The presented system is tested with up to four mixed human voices. This system separates only the main speaker voice and processes it prior to the transmission over VoIP call. This paper illustrates the algorithm technologies integration using DNN, and voice signal processing advantages and challenges, in addition to the importance of computing power for real-time applications.

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Sabir, Brahim, Fatima Rouda, Yassine Khazri, Bouzekri Touri, and Mohamed Moussetad. "Improved Algorithm for Pathological and Normal Voices Identification." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (February 1, 2017): 238. http://dx.doi.org/10.11591/ijece.v7i1.pp238-243.

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There are a lot of papers on automatic classification between normal and pathological voices, but they have the lack in the degree of severity estimation of the identified voice disorders. Building a model of pathological and normal voices identification, that can also evaluate the degree of severity of the identified voice disorders among students. In the present work, we present an automatic classifier using acoustical measurements on registered sustained vowels /a/ and pattern recognition tools based on neural networks. The training set was done by classifying students’ recorded voices based on threshold from the literature. We retrieve the pitch, jitter, shimmer and harmonic-to-noise ratio values of the speech utterance /a/, which constitute the input vector of the neural network. The degree of severity is estimated to evaluate how the parameters are far from the standard values based on the percent of normal and pathological values. In this work, the base data used for testing the proposed algorithm of the neural network is formed by healthy and pathological voices from German database of voice disorders. The performance of the proposed algorithm is evaluated in a term of the accuracy (97.9%), sensitivity (1.6%), and specificity (95.1%). The classification rate is 90% for normal class and 95% for pathological class.

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Brahim, Sabir, Rouda Fatima, Khazri Yassine, Touri Bouzekri, and Moussetad Mohamed. "Improved Algorithm for Pathological and Normal Voices Identification." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 1 (February 1, 2017): 238–43. https://doi.org/10.11591/ijece.v7i1.pp238-243.

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There are a lot of papers on automatic classification between normal and pathological voices, but they have the lack in the degree of severity estimation of the identified voice disorders. Building a model of pathological and normal voices identification, that can also evaluate the degree of severity of the identified voice disorders among students. In the present work, we present an automatic classifier using acoustical measurements on registered sustained vowels /a/ and pattern recognition tools based on neural networks. The training set was done by classifying students’ recorded voices based on threshold from the literature. We retrieve the pitch, jitter, shimmer and harmonic-to-noise ratio values of the speech utterance /a/, which constitute the input vector of the neural network. The degree of severity is estimated to evaluate how the parameters are far from the standard values based on the percent of normal and pathological values. In this work, the base data used for testing the proposed algorithm of the neural network is formed by healthy and pathological voices from German database of voice disorders. The performance of the proposed algorithm is evaluated in a term of the accuracy (97.9%), sensitivity (1.6%), and specificity (95.1%). The classification rate is 90% for normal class and 95% for pathological class.

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Kilgore, Ryan, and Mark Chignell. "Simple Visualizations Enhance Speaker Identification when Listening to Spatialized Voices." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 4 (September 2005): 615–18. http://dx.doi.org/10.1177/154193120504900403.

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Spatial audio has been demonstrated to enhance performance in a variety of listening tasks. The utility of visually reinforcing spatialized audio with depictions of voice locations in collaborative applications, however, has been questioned. In this experiment, we compared the accuracy, response time, confidence in task performance, and subjective mental workload of 18 participants in a voice-identification task under three different display conditions: 1) traditional mono audio; 2) spatial audio; 3) spatial audio with a visual representation of voice locations. Each format was investigated using four and eight unique stimuli voices. Results showed greater voice-identification accuracy for the spatial-plus-visual format than for the spatialand mono-only formats, and that visualization benefits increased with voice number. Spatialization was also found to increase confidence in task performance. Response time and mental workload remained unchanged across display conditions. These results indicate visualizations may benefit users of large, unfamiliar audio spaces.

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R Hanji, Bhagyashri, Sanjay T. J, Shivam Upadhyay, Tarun M, and Yashwanthgowda H. R. "Voice Grounded Gender Identification." Journal of Web Development and Web Designing 05, no. 02 (July 1, 2020): 20–25. http://dx.doi.org/10.46610/jowdwd.2020.v05i02.004.

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Fujimoto, Junichiroh. "Identification of voice pattern." Journal of the Acoustical Society of America 94, no. 6 (December 1993): 3539. http://dx.doi.org/10.1121/1.407114.

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Ladefoged, Peter. "Validity of voice identification." Journal of the Acoustical Society of America 114, no. 4 (October 2003): 2403. http://dx.doi.org/10.1121/1.4778312.

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Et. al., Manasi Bendale,. "Voice Based Disease Identification System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 2021): 96–105. http://dx.doi.org/10.17762/turcomat.v12i1s.1568.

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Human voice as well as the sound of the body is used as a clinical method to assess the health condition of an individual. The evaluation of the human voice has risen as a critical field of exploration. Speech analysis fundamentally involves the extraction of certain features from voice signals for generation of voice in alluring pertinence by using reasonable techniques. This paper brings up normal ailments that sway understanding voice patterns in proof for driving research that have affirmed voice modifications as demonstrative manifestations in their respective ailments and also the technique by which voice analysis can be done.

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Ye, Yongchao, Lingjie Lao, Diqun Yan, and Rangding Wang. "Identification of Weakly Pitch-Shifted Voice Based on Convolutional Neural Network." International Journal of Digital Multimedia Broadcasting 2020 (January 6, 2020): 1–10. http://dx.doi.org/10.1155/2020/8927031.

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Pitch shifting is a common voice editing technique in which the original pitch of a digital voice is raised or lowered. It is likely to be abused by the malicious attacker to conceal his/her true identity. Existing forensic detection methods are no longer effective for weakly pitch-shifted voice. In this paper, we proposed a convolutional neural network (CNN) to detect not only strongly pitch-shifted voice but also weakly pitch-shifted voice of which the shifting factor is less than ±4 semitones. Specifically, linear frequency cepstral coefficients (LFCC) computed from power spectrums are considered and their dynamic coefficients are extracted as the discriminative features. And the CNN model is carefully designed with particular attention to the input feature map, the activation function and the network topology. We evaluated the algorithm on voices from two datasets with three pitch shifting software. Extensive results show that the algorithm achieves high detection rates for both binary and multiple classifications.

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Xuetong, Bai, and Wang Qiuyun. "Challenges of DeepFake Voice to Voice Forensic Identification." Criminal Justice Science & Governance 2, no. 2 (2021): 86–92. http://dx.doi.org/10.35534/cjsg.0202012.

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Selvakumari, N. A. Sheela, and V. Radha. "Voice Pathology Identification: A Survey on Voice Disorder." International Journal of Engineering and Manufacturing 7, no. 2 (March 8, 2017): 39–49. http://dx.doi.org/10.5815/ijem.2017.02.04.

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Lee, Bum Joo, Dong Uk Cho, and Yeon Man Jeong. "Identification of Voice Features for Recently Voice Fishing by Voice Analysis." Journal of Korean Institute of Communications and Information Sciences 41, no. 10 (October 31, 2016): 1276–83. http://dx.doi.org/10.7840/kics.2016.41.10.1276.

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Chen, Meng, Li Lu, Junhao Wang, Jiadi Yu, Yingying Chen, Zhibo Wang, Zhongjie Ba, Feng Lin, and Kui Ren. "VoiceCloak." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 2 (June 12, 2023): 1–21. http://dx.doi.org/10.1145/3596266.

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Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma when enjoying the utility of voice services. Existing machine-centric studies employ direct modification or text-based re-synthesis to de-identify users' voices but cause inconsistent audibility for human participants in emerging online communication scenarios, such as virtual meetings. In this paper, we propose a human-centric voice de-identification system, VoiceCloak, which uses adversarial examples to balance the privacy and utility of voice services. Instead of typical additive examples inducing perceivable distortions, we design a novel convolutional adversarial example that modulates perturbations into real-world room impulse responses. Benefiting from this, VoiceCloak could preserve user identity from exposure by Automatic Speaker Identification (ASI), while remaining the voice perceptual quality for non-intrusive de-identification. Moreover, VoiceCloak learns a compact speaker distribution through a conditional variational auto-encoder to synthesize diverse targets on demand. Guided by these pseudo targets, VoiceCloak constructs adversarial examples in an input-specific manner, enabling any-to-any identity transformation for robust de-identification. Experimental results show that VoiceCloak could achieve over 92% and 84% successful de-identification on mainstream ASIs and commercial systems with excellent voiceprint consistency, speech integrity, and audio quality.

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Karenina, Vita, Moh Fiqih Erinsyah, and Dega Surono Wibowo. "Klasifikasi Rentang Usia Dan Gender Dengan Deteksi Suara Menggunakan Metode Deep Learning Algoritma Cnn (Convolutional Neural Network)." Komputika : Jurnal Sistem Komputer 12, no. 2 (September 21, 2023): 75–82. http://dx.doi.org/10.34010/komputika.v12i2.10516.

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The research discusses the identification of human voices based on gender by utilizing the differences in voice characteristics between males and females. In addition to differences in vocal tract size, factors such as length, thickness, and stiffness of the vocal cords also play a role in producing the differences in fundamental frequency between the two genders. Fundamental frequency serves as an indicator used in acoustic analysis to classify gender based on voice. In the automatic classification of voices, sound processing techniques and machine learning are key in system development. Gender recognition methods based on voice involve acoustic analysis using voice features such as fundamental frequency, formants, duration, intensity, and intonation patterns. Diverse voice datasets containing recordings of both male and female voices are used to train gender recognition models. From the results of research from modeling using CNN on audio to get 92% accuracy and for testing results it is good enough in classifying. Keywords – Deep Learning, Voice Recognition, Audio Classification, CNN, Gender

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Shigeno, Sumi. "Speaking with a Happy Voice Makes You Sound Younger." International Journal of Psychological Studies 8, no. 4 (October 25, 2016): 71. http://dx.doi.org/10.5539/ijps.v8n4p71.

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<p>This study investigates the effects of emotional voices (expressing neutral emotion, sadness, and happiness) on a judgement of a speaker’s age. An experiment was conducted to explore whether happy voices sound younger than neutral and sad voices. The identification of 24 speakers’ ages (12 of each gender) based on their emotional voices was done by 40 participants. The speakers’ ages were 24-75 years. Participants identified the age of each speaker only by hearing his/her emotional voice. The results showed that when a speaker spoke with a happy voice, participants estimated their age to be younger than their chronological age. Furthermore, the results regarding female happy voices were more conspicuous than male happy voices. In contrast, when a speaker spoke with a sad voice, participants estimated them to be older. The results suggest that a happy voice sounds younger because of its higher voice pitch (<em>F0</em>). We discussed the role of vocal pitch and other paralinguistic factors for providing an aging impression.</p>

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Orken, Mamyrbayev, Kydyrbekova Aizat, Alimhan Keylan, Oralbekova Dina, Zhumazhanov Bagashar, and Nuranbayeva Bulbul. "Development of security systems using DNN and i & x-vector classifiers." Eastern-European Journal of Enterprise Technologies 4, no. 9 (112) (August 31, 2021): 32–45. https://doi.org/10.15587/1729-4061.2021.239186.

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The widespread use of biometric systems entails increased interest from cybercriminals aimed at developing attacks to crack them. Thus, the development of biometric identification systems must be carried out taking into account protection against these attacks. The development of new methods and algorithms for identification based on the presentation of randomly generated key features from the biometric base of user standards will help to minimize the disadvantages of the above methods of biometric identification of users. We present an implementation of a security system based on voice identification as an access control key and a verification algorithm developed using MATLAB function blocks that can authenticate a person's identity by his or her voice. Our research has shown an accuracy of 90 % for this user identification system for individual voice characteristics. It has been experimentally proven that traditional MFCCs using DNN and i and x-vector classifiers can achieve good results. The paper considers and analyzes the most well-known approaches from the literature to the problem of user identification by voice: dynamic programming methods, vector quantization, mixtures of Gaussian processes, hidden Markov model. The developed software package for biometric identification of users by voice and the method of forming the user's voice standards implemented in the complex allows reducing the number of errors in identifying users of information systems by voice by an average of 1.5 times. Our proposed system better defines voice recognition in terms of accuracy, security and complexity. The application of the results obtained will improve the security of the identification process in information systems from various attacks.

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Nugroho, Okvi, Opim Salim Sitompul, and Suherman Suherman. "Identification of Regional Origin Based on Dialec Using the Perceptron Evolving Multilayer Method." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 3 (July 31, 2023): 1602. http://dx.doi.org/10.30865/mib.v7i3.6301.

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Voice detection is very important for the world of information technology that can be used for voice processing, biometrics, human computer interfaces. Voice identification carried out in this study is based on speech or dialect using a prototype that has been designed using the Raspiberry Pi device and other supporting devices. In its application, the regional identification prototype uses sound feature extraction, namely Mel Frequency Cepstral Coefficients (MFCC) and uses an artificial neural network method with a multilayer perceptron (secos) developing algorithm. The purpose of this study is to identify regional origins based on dialect or speech using the Mel Frequency Cepstral Coefficients (MFCC) extraction technique and the Evolving Multilayer Perceptron method. The results of the regional recognition test produce a good level of accuracy, with testing as an example of the Aceh area with test data of 10 voice samples, the results obtained by the prototype can identify voices with a success rat of being able to recognize 7 voices out of 10 samples tested in the Aceh region. From all the tests on the areas of Aceh, Karo, Nias, Simalungun, the accuracy was 88%

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Siegenthaler, Perina, and Andreas Fahr. "First-Person Versus Third-Person." European Journal of Health Communication 4, no. 3 (October 5, 2023): 35–52. http://dx.doi.org/10.47368/ejhc.2023.303.

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Research on health communication shows that audience involvement with media characters displayed in narratives represents a key mechanism that facilitates persuasive outcomes. This study analyses whether different narrative voices trigger identification with story characters and affect counter-arguments against and attitudes toward proposed recommendations. The online experiment (N = 364) investigates the effects of first- and third-person narrative voice in explainer videos using the example of work-related stress, and similarities between the audience’s situations and that of the character are accounted for. The moderated mediation analysis showed no effect of narrative voice on identification and being personally affected by the health issue addressed in the explainer video did not play a moderating role. Furthermore, the results showed that identification was negatively associated with disagreement and positively related to attitudes toward recommendations. Narrative voice did not have a direct impact on attitudes and there is was no effect mediated via identification and disagreement.

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Kim, Seung Min, Dae Eol Park, and Dae Seon Choi. "Comparison of Korean Speech De-identification Performance of Speech De-identification Model and Broadcast Voice Modulation." Korean Institute of Smart Media 12, no. 2 (March 30, 2023): 56–65. http://dx.doi.org/10.30693/smj.2023.12.2.56.

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In broadcasts such as news and coverage programs, voice is modulated to protect the identity of the informant. Adjusting the pitch is commonly used voice modulation method, which allows easy voice restoration to the original voice by adjusting the pitch. Therefore, since broadcast voice modulation methods cannot properly protect the identity of the speaker and are vulnerable to security, a new voice modulation method is needed to replace them. In this paper, using the Lightweight speech de-identification model as the evaluation target model, we compare speech de-identification performance with broadcast voice modulation method using pitch modulation. Among the six modulation methods in the Lightweight speech de-identification model, we experimented on the de-identification performance of Korean speech as a human test and EER(Equal Error Rate) test compared with broadcast voice modulation using three modulation methods: McAdams, Resampling, and Vocal Tract Length Normalization(VTLN). Experimental results show VTLN modulation methods performed higher de-identification performance in both human tests and EER tests. As a result, the modulation methods of the Lightweight model for Korean speech has sufficient de-identification performance and will be able to replace the security-weak broadcast voice modulation.

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Harnsberger, James, and Harry Hollien. "Selection of speech/voice vectors in forensic voice identification." Journal of the Acoustical Society of America 130, no. 4 (October 2011): 2546. http://dx.doi.org/10.1121/1.3655179.

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Kyrychenko, Oleh, and Oleksandr Khrystov. "Organisational-tactical peculiarities of using public representatives in the course of presentation for voice or speech recognition." Collection of Ukrainian Research Institute of Special Equipment and Forensic Expertise of the Security Service OF Ukraine, no. 1 (January 28, 2021): 33–44. http://dx.doi.org/10.54658/ssu.27097978.2021.1.pp.33-44.

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The article has identified the main organizational, tactical and legal issues of using members of the public during the presentation for recognition by voice or speech, including phonogram. The authors have concluded that the most effective in terms of organizational implementation of this investigative (search) action, in terms of involvement of mutes, experts, witnesses, there is always lack of cooperation with NGOs, volunteers, representatives of labor collectives and cultural community. They have developed and proposed algorithms for using members of the public as: 1) persons who are presented together with the suspect as «mutes» for identification by «live» voice; 2) persons who perform the role of a source for the creation of audio samples of the voice (when recognizing by phonogram); 3)professionals to create audio samples of the voice (when recognizing by phonogram); 4) professionals to establish possible changes in the voice of the person to be identified; 5) witnesses involved in order to certify the correctness and objectivity of the recording of the content, course and results of the presentation for recognition by voice or speech. For this purpose typical investigative situations that arise in the case of the use of members of the public during the presentation for identification by voice and speech have been identified, in particular: 1. In the use of members of the public as persons who are presented together with the suspect as «extras» for identification by a «live» voice, typical investigative situations include: 1) discrepancy between the characteristics of the voice (volume, intelligibility of speech, timbre, gender of the person, etc.) of colleagues or persons who are in «close access» and the characteristics of the person’s voice presented for identification; 2) refusal of colleagues or persons who are in «close access» to participate in this investigative (search) action as a mute, etc.; 3) the investigator’s inability to determine the characteristics of mutes’ voices at the stage of preparation for the investigative (search) action. 2. When using members of the public as persons who act as a source for the creation of audio samples of the voice (when recognizing by phonogram), the authors have identified the following typical investigative situations: 1) the sound of the created phonogram (signal) does not correspond to «living» voices, or is perceived differently; 2) the number of pronounced words and phrases or their sequence is insufficient for recognition by the recognizing person, etc. 3. When using members of the public as specialists to create audio samples of the voice (when recognizing by phonogram), the following tactical situations may arise: 1) lack of opportunity to involve an expert; 2) conducting an investigative (search) action in remote settlements.

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Jung, Yongsuhk, and Yongjun Choi. "Voices of Loyal Members: Dual Role of Organizational Identification in the Process of Employee Voice." Behavioral Sciences 15, no. 2 (January 22, 2025): 109. https://doi.org/10.3390/bs15020109.

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Based on Hirschman’s theory of loyalty and Packer’s normative conflict model, the present study examined the roles of organizational identification in the voice emergence and reaction processes, wherein individuals provide voice and receive evaluations for their voice behavior, respectively. Using a survey method, data were collected from 455 cadets and their supervisors at a military educational institute in South Korea, who live and work together under an honor-based organizational system that encourages voice behavior through formal and informal channels. Structural equation modeling (SEM) was used for hypothesis testing. Our findings from multi-source data demonstrated that, when controlling for two social exchange variables (i.e., leader–member exchange and perceived organizational support), organizational identification not only increases voice behavior but also strengthens the positive relationship between voice behavior and supervisor performance evaluations. Specifically, voice behavior has a positive relationship with performance appraisal only when organizational identification is high. Theoretical and practical implications of the findings and directions for future research are discussed.

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Ramani, Sai Aishwarya, Eric J. Hunter, and Lady Catherine Cantor Cutiva. "Acoustic speech parameter relationships with voice disorders and phrase differences." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A295. http://dx.doi.org/10.1121/10.0018908.

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While acoustic speech analysis is non-invasive, the utility has been mixed due to the range of voice types. For vocal health practitioners to efficiently and quickly assess and document voice changes, knowing which voice parameter would be sensitive to vocal change is crucial. Using a database of 296 individual voices including 8 voice pathology types and typical voice samples, the sensitivity of a range of acoustic speech parameters to differentiate common voice pathology types was investigated. Both traditional and contemporary acoustic speech metrics were estimated for the samples using a custom MATLAB script and PRAAT (e.g., jitter, shimmer HNR, CPPS, Alpha ratio, PPE). Analysis then evaluate the predictability value of the metrics to discriminate pathology type. From the pool of parameters, 11 were able to identify pathological voices from normal controls and several of the parameters were more sensitive to some pathology. For example, CPPs and jitter values could discriminate neuropathological voices whereas HNR and Shimmer cold discriminate muscle-based pathologies. These results indicate how the sensitivity of acoustic speech metrics to the voice pathology types can allow for the identification of individual metrics (or combinations of metrics) which could be used to track changes in vocal health.

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Rácz, József, Zsuzsa Kaló, Szilvia Kassai, Márta Kiss, and Judit Nóra Pintér. "The experience of voice hearing and the role of self-help group: An interpretative phenomenological analysis." International Journal of Social Psychiatry 63, no. 4 (March 27, 2017): 307–13. http://dx.doi.org/10.1177/0020764017700926.

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Background: Auditory verbal hallucinations (AVHs) played an important role in the psychiatric diagnostics, but in the last few decades the diagnostic-free complex phenomenological understanding of the phenomena of voice hearing became the focus of studies. Materials: Six semi-structured interviews with recovering voice hearers were conducted and analysed using interpretative phenomenological analysis (IPA). Discussion: The self-help group gives significant help in identification and dealing with the voices; therefore, it serves as turning point in the life story of voice hearers. Conclusion: Applying self-help group in clinical context contributes to better outcomes in treatment of voice hearers.

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., Li Jing, Huang Hua ., and Liu Sufang . "Voice Identification Based on HMMs." Trends in Applied Sciences Research 1, no. 1 (January 1, 2006): 79–82. http://dx.doi.org/10.3923/tasr.2006.79.82.

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Engelberg, Saidoff, and Israeli. "Voice identification through spectral analysis." IEEE Instrumentation and Measurement Magazine 9, no. 6 (October 2006): 52–55. http://dx.doi.org/10.1109/mim.2006.1708353.

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Didla, Grace S., and Dr Harry Hollien. "Voice disguise and speaker identification." Journal of the Acoustical Society of America 138, no. 3 (September 2015): 1808. http://dx.doi.org/10.1121/1.4933738.

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Boë, Louis-Jean. "Forensic voice identification in France." Speech Communication 31, no. 2-3 (June 2000): 205–24. http://dx.doi.org/10.1016/s0167-6393(99)00079-5.

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Yarmey, A. Daniel. "Voice Identification Over the Telephone1." Journal of Applied Social Psychology 21, no. 22 (November 1991): 1868–76. http://dx.doi.org/10.1111/j.1559-1816.1991.tb00510.x.

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Bohan, Thomas L. "Review of: Forensic Voice Identification." Journal of Forensic Sciences 49, no. 4 (2004): 1–2. http://dx.doi.org/10.1520/jfs2004083.

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Yarmey, A. Daniel, and Eva Matthys. "Voice identification of an abductor." Applied Cognitive Psychology 6, no. 5 (September 1992): 367–77. http://dx.doi.org/10.1002/acp.2350060502.

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Simo, Zsuzsa. "Topic Identification in Voice Recordings." Acta Marisiensis. Seria Technologica 20, no. 2 (December 1, 2023): 43–48. http://dx.doi.org/10.2478/amset-2023-0017.

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Abstract The paper shows the understanding of a topic recognition problem like the speech recognition system based on Natural Language Processing (NLP) and the steps of its implementation of a rules-based approach, which is able to classify given audio materials based on predefined topics in real-time. During implementation, a statistical vocabulary was developed. Google Speech API (Application Programming Interface) was employed for subtitling audio materials, and the most ideal time frame for reception was identified through several experiments. The motivation of this work is based on the deficiency of similar simple systems for Hungarian topic recognition, even though numerous international languages already utilize multiple Automatic Sound Recognition (ASR) systems.

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BIRCHWOOD, M., A. MEADEN, P. TROWER, P. GILBERT, and J. PLAISTOW. "The power and omnipotence of voices: subordination and entrapment by voices and significant others." Psychological Medicine 30, no. 2 (March 2000): 337–44. http://dx.doi.org/10.1017/s0033291799001828.

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Background. Cognitive therapy for psychotic symptoms often embraces self-evaluative beliefs (e.g. self-worth) but whether and how such beliefs are related to delusions remains uncertain. In previous research we demonstrated that distress arising from voices was linked to beliefs about voices and not voice content alone. In this study we examine whether the relationship with the voice is a paradigm of social relationships in general, using a new framework of social cognition, ‘ranking’ theory.Method. In a sample of 59 voice hearers, measures of power and social rank difference between voice and voice hearer are taken in addition to parallel measures of power and rank in wider social relationships.Results. As predicted, subordination to voices was closely linked to subordination and marginalization in other social relationships. This was not the result of a mood-linked appraisal. Distress arising from voices was linked not to voice characteristics but social and interpersonal cognition.Conclusion. This study suggests that the power imbalance between the individual and his persecutor(s) may have origins in an appraisal by the individual of his social rank and sense of group identification and belonging. The results also raise the possibility that the appraisal of voice frequency and volume are the result of the appraisal of voices' rank and power. Theoretical and novel treatment implications are discussed.

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Rachman, Laura, Almut Jebens, and Deniz Baskent. "Phonological but not lexical processing alters the perceptual weighting of mean fundamental frequency and vocal-tract length cues for voice gender categorisation." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A262. http://dx.doi.org/10.1121/10.0011271.

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Listeners use various voice cues to segregate different speakers, or to infer speaker-related information such as perceived gender. Two important anatomically related voices cues used for speaker identification, including perceived gender, are mean fundamental frequency (F0), related to the glottal pulse rate, and vocal-tract length (VTL), correlating with body size. Voice cue processing seems to be affected by linguistic processes, such that voice perception is more precise when listeners hear speakers in their native language compared to a non-native language. In addition, recent research shows that F0 and VTL sensitivity is lower for words compared to time-reversed words, either because time-reversed words are unintelligible or phonemes are distorted in voice-onset times and aspirations, pointing to effects of lexical or phonological processing. However, voice cue sensitivity and using these cues to infer speaker-related information may rely on different mechanisms. Here, we studied effects of lexical and phonological processing on F0 and VTL cue weighting for one aspect of speaker identification, namely voice gender categorisation, by manipulating these cues in three linguistic conditions: meaningful words; phonotactically plausible nonwords; and phonotactically implausible time-reversed nonwords. We found that F0 and VTL weighting for voice gender categorisation was affected by phonological but not by lexical processing.

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Deng, Xuefei (Nancy), Joseph Taylor, and K. D. Joshi. "To Speak up or Shut up? Revealing the Drivers of Crowdworker Voice Behaviors in Crowdsourcing Work Environments." Communications of the Association for Information Systems 53, no. 1 (2023): 1003–27. http://dx.doi.org/10.17705/1cais.05343.

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This study examines worker voice behaviors in the microtask crowdsourcing work environment (CSWE) where voice channels are absent. Informed by employee voice research, this study adopts the revealed causal mapping method to analyze the detailed narratives of 60 workers from Amazon Mechanical Turk. Our data analysis shows that the crowdworkers did engage in voice behaviors, but their voices were not always heard, depending on recipients. The crowdworker voice was directed to three different recipients (worker community, job requester, and platform) and influenced by six antecedents (duty orientation, efficacy judgment, workgroup identification, anger/frustration, futility, and achievement orientation). Based on the findings, we propose a model of worker voice antecedents and moderators in the CSWE. This study extends employee voice research by presenting a moderator perspective in the CSWE. Moreover, our study provides a nuanced understanding of crowdworker voice behaviors from two major aspects – antecedent and recipient – contributing to crowdsourcing research.

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Shen, Lue, Yuxuan Wang, Patrick Wong, and Tyler K. Perrachione. "Acoustic divergence from the training sample determines talker identification accuracy for emotional voices." Journal of the Acoustical Society of America 155, no. 3_Supplement (March 1, 2024): A264. http://dx.doi.org/10.1121/10.0027444.

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Different emotional states introduce substantial acoustic variations in talkers’ voices. It remains unclear how within-talker variability across emotional states affects listeners' ability to maintain perceptual constancy during talker identification. Here, we investigated (1) how changes in talkers’ emotional state affected talker identification accuracy, (2) how emotional state affected key features of voice acoustics, and (3) how emotion-related changes in these acoustic features affected listeners’ talker identification performance. Forty-eight listeners learned to identify talkers from speech expressing one emotional state (neutral, fearful, or angry) and then attempted to generalize that knowledge to speech expressing another emotional state. Talker identification accuracy was significantly worse in untrained emotions. Changes in voice acoustics across emotions were characterized for mean F0, F0 variability, jitter, HNR, speaking rate, and mean F2. To determine how emotion-related acoustic changes affected talker identification, we modeled talker identification accuracy as a function of difference in these features between training and test stimuli. Accuracy decreased as acoustic differences increased, regardless of talkers’ emotion. Thus, perceptual constancy depends on acoustic similarity to prior experience with a talker’s voice. Larger acoustic deviations, like those introduced by changes in emotional state, are more likely to cause a listener to misidentify a talker.

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Nikmah, Asrivatun, Auli Damayanti, and Edi Winarko. "Voice-Based Emotion Identification Based on Mel Frequency Cepstral Coefficient Feature Extraction Using Self-Organized Maps and Radial Basis Function." Contemporary Mathematics and Applications (ConMathA) 7, no. 1 (March 27, 2025): 36–45. https://doi.org/10.20473/conmatha.v7i1.68246.

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Speech recognition is one of the most popular research fields, one of which is about emotion identification. Voice-based emotion identification is carried out to determine the pattern of emotions using the depth analysis mechanism of voice signal development and feature extraction that carries the emotional characteristic parameters of the speaker's voice. Furthermore, the emotional characteristics of the speaker's voice are classified using an artificial neural network method to recognize patterns. In this study, emotion identification from voice signal data is classified into angry, sad, happy, and neutral emotions. The stages of voice-based emotion identification, including the feature extraction stage using the mel frequency cepstral coefficient, produce coefficient values, which will be used in the identification stage using the Self Organized Maps method on the Radial Basis Function.

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Zhou, Yinghui, Yali Liu, and Huan Niu. "Perceptual Characteristics of Voice Identification in Noisy Environments." Applied Sciences 12, no. 23 (November 27, 2022): 12129. http://dx.doi.org/10.3390/app122312129.

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Auditory analysis is an essential method that is used to recognize voice identity in court investigations. However, noise will interfere with auditory perception. Based on this, we selected white noise, pink noise, and speech noise in order to design and conduct voice identity perception experiments. Meanwhile, we explored the impact of the noise type and frequency distribution on voice identity perception. The experimental results show the following: (1) in high signal-to-noise ratio (SNR) environments, there is no significant difference in the impact of noise types on voice identity perception; (2) in low SNR environments, the perceived result of speech noise is significantly different from that of white noise and pink noise, and the interference is more obvious; (3) in the speech noise with a low SNR (−8 dB), the voice information contained in the high-frequency band of 2930~6250 Hz is helpful for achieving accuracy in voice identity perception. These results show that voice identity perception in a better voice transmission environment is mainly based on the acoustic information provided by the low-frequency and medium-frequency bands, which concentrate most of the energy of the voice. As the SNR gradually decreases, a human’s auditory mechanism will automatically expand the receiving frequency range to obtain more effective acoustic information from the high-frequency band. Consequently, the high-frequency information ignored in the objective algorithm may be more robust with respect to identity perception in our environment. The experimental studies not only evaluate the quality of the case voice and control the voice recording environment, but also predict the accuracy of voice identity perception under noise interference. This research provides the theoretical basis and data support for applying voice identity perception in forensic science.

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Ramadhina, Dea Sifana, Rita Magdalena, and Sofia Saidah. "Individual Identification Through Voice Using Mel-Frequency Cepstrum Coefficient (MFCC) and Hidden Markov Models (HMM) Method." Journal of Measurements, Electronics, Communications, and Systems 7, no. 1 (December 30, 2020): 26. http://dx.doi.org/10.25124/jmecs.v7i1.3553.

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Voice is one of the parameters in the identification process of a person. Through the voice, information will be obtained such as gender, age, and even the identity of the speaker. Speaker recognition is a method to narrow down crimes and frauds committed by voice. So that it will minimize the occurrence of faking one's identity. The Method of Mel Frequency Cepstrum Coefficient (MFCC) can be used in the speech recognition system. The process of feature extraction of speech signal using MFCC will produce acoustic speech signal. The classification, Hidden Markov Models (HMM) is used to match unidentified speaker’s voice with the voices in database. In this research, the system is used to verify the speaker, namely 15 text dependent in Indonesian. On testing the speaker with the same as database, the highest accuracy is 99,16%.

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Li, Jie, Qiaozhuan Liang, Zhenzhen Zhang, and Xiao Wang. "Leader humility and constructive voice behavior in China: a dual process model." International Journal of Manpower 39, no. 6 (September 3, 2018): 840–54. http://dx.doi.org/10.1108/ijm-06-2017-0137.

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PurposeThe purpose of this paper is to find how leader humility affects employees’ constructive voice behavior toward supervisor (speaking up) and coworkers (speaking out) from an identification-based perspective, and seeks to verify the effectiveness of leader humility in the Chinese context.Design/methodology/approachData were collected from 325 employees in four Chinese companies with two phases. In the first phase, the participants were asked to report the leader humility, their identification of their relations with the supervisor, and their identification with their organization. In the second phase, they were asked to report their voice behaviors toward their supervisors and coworkers.FindingsThe results indicate that leader humility strongly predicts both employees’ voice behaviors of speaking up and speaking out. Results further suggest that relational identification with the supervisor explains why leader humility promotes employees speaking up, while organizational identification explains why leader humility promotes employees speaking up and speaking out.Practical implicationsManagers with humility can successfully shape employees’ relational and organizational identifications, which in turn encourage their voice behaviors toward supervisors and coworkers. Hence, behaving humbly in working places could be an effective way for managers to promote organizational cohesion and creativity.Originality/valueAlthough leader humility attracts much attention in both academia and practice, researchers have been primarily focusing on conceptual development and measurement issues, and empirical studies are rare. This is the first research connecting leader humility and employee proactive behaviors. Moreover, it takes an in-depth analysis of the constructive voice behaviors by differentiating them based on their targets.

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Jaleel, Hanan Qassim, Jane Jaleel Stephan, and Sinan A. Naji. "Gender Identification from Speech Recognition Using Machine Learning Techniques and Convolutional Neural Networks." Webology 19, no. 1 (January 20, 2022): 1666–88. http://dx.doi.org/10.14704/web/v19i1/web19112.

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Gender identification represents a fundamental component of speech recognition and automatic interacting sound responding systems. Identifying the voice gender minimizes the computational loads of these systems for additional processing. Standard approaches for gender estimation from the speech have broadly relied on the extraction of speech features and classification tasks. This paper proposes a technique for gender identification of speech samples using the speech recognition process. The proposed technique extracts essential voice features like Mean, Zero-Crossing, Standard Deviation, and Amplitude, as well as 12 most significant features from every voice sample, and combines them to create voice feature vectors. The proposed technique uses several machine and deep learning methods such as Random Forest, KNN, Logistic Regression, Decision Tree, and CNNs, in order to classify the voice vectors into Male and Female classes. After comparing the evaluation metrics results of all classifiers, the proposed technique finds out that the CNN model is the best classifier used to classify the voice vectors with a higher precision value of 1.0.

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Amalia, Sitti. "Identification Of Number Using Artificial Neural Network Backpropagation." MATEC Web of Conferences 215 (2018): 01011. http://dx.doi.org/10.1051/matecconf/201821501011.

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This research proposed to design and implementation system of voice pattern recognition in the form of numbers with offline pronunciation. Artificial intelligent with backpropagation algorithm used on the simulation test. The test has been done to 100 voice files which got from 10 person voices for 10 different numbers. The words are consisting of number 0 to 9. The trial has been done with artificial neural network parameters such as tolerance value and the sum of a neuron. The best result is shown at tolerance value varied and a sum of the neuron is fixed. The percentage of this network training with optimal architecture and network parameter for each training data and new data are 82,2% and 53,3%. Therefore if tolerance value is fixed and a sum of neuron varied gave 82,2% for training data and 54,4% for new data

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Lutsenko, K., and K. Nikulin. "VOICE SPEAKER IDENTIFICATION AS ONE OF THE CURRENT BIOMETRIC METHODS OF IDENTIFICATION OF A PERSON." Theory and Practice of Forensic Science and Criminalistics 19, no. 1 (April 2, 2020): 239–55. http://dx.doi.org/10.32353/khrife.1.2019.18.

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The article deals with the most widespread biometric identification systems of individuals, including voice recognition of the speaker on video and sound recordings. The urgency of the topic of identification of a person is due to the active informatization of modern society and the increase of flows of confidential information. The branches of the use of biometric technologies and their general characteristics are given. Here is an overview of the use of identification groups that characterize the voice. Also in the article the division of voice identification systems into the corresponding classes is given. The main advantages of voice biometrics such as simplicity of system realization are considered; low cost (the lowest among all biometric methods); No need for contact, the voice biometry allows for long-range verification, unlike other biometric technologies. The analysis of existing methods of speech recognition recognition identifying a person by a combination of unique voice characteristics, determining their weak and strong points, on the basis of which the choice of the most appropriate method for solving the problem of text-independent recognition, Namely the model of Gaussian mixtures, was carried out. The prerequisite for the development of speech technologies is a significant increase in computing capabilities, memory capacity with a significant reduction in the size of computer systems. It should also be Noted the development of mathematical methods that make it possible to perform the Necessary processing of an audio signal by isolating informative features from it. It has been established that the development of information technologies, and the set of practical applications, which use voice recognition technologies, make this area relevant for further theoretical and practical research.

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Mamyrbayev, Orken, Aizat Kydyrbekova, Keylan Alimhan, Dina Oralbekova, Bagashar Zhumazhanov, and Bulbul Nuranbayeva. "Development of security systems using DNN and i & x-vector classifiers." Eastern-European Journal of Enterprise Technologies 4, no. 9(112) (August 31, 2021): 32–45. http://dx.doi.org/10.15587/1729-4061.2021.239186.

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The widespread use of biometric systems entails increased interest from cybercriminals aimed at developing attacks to crack them. Thus, the development of biometric identification systems must be carried out taking into account protection against these attacks. The development of new methods and algorithms for identification based on the presentation of randomly generated key features from the biometric base of user standards will help to minimize the disadvantages of the above methods of biometric identification of users. We present an implementation of a security system based on voice identification as an access control key and a verification algorithm developed using MATLAB function blocks that can authenticate a person's identity by his or her voice. Our research has shown an accuracy of 90 % for this user identification system for individual voice characteristics. It has been experimentally proven that traditional MFCCs using DNN and i and x-vector classifiers can achieve good results. The paper considers and analyzes the most well-known approaches from the literature to the problem of user identification by voice: dynamic programming methods, vector quantization, mixtures of Gaussian processes, hidden Markov model. The developed software package for biometric identification of users by voice and the method of forming the user's voice standards implemented in the complex allows reducing the number of errors in identifying users of information systems by voice by an average of 1.5 times. Our proposed system better defines voice recognition in terms of accuracy, security and complexity. The application of the results obtained will improve the security of the identification process in information systems from various attacks.

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Journal articles on the topic 'Voice identification'

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