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Journal articles on the topic 'Audio speaker'

Author: Grafiati

Published: 6 September 2023

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1

Burton, Paul. "Audio speaker." Journal of the Acoustical Society of America 89, no. 1 (1991): 495. http://dx.doi.org/10.1121/1.400405.

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2

Tsuda, Shiro. "Audio speaker and method for assembling an audio speaker." Journal of the Acoustical Society of America 118, no. 2 (2005): 589. http://dx.doi.org/10.1121/1.2040247.

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Tsuda, Shiro. "Audio speaker and method for assembling an audio speaker." Journal of the Acoustical Society of America 123, no. 2 (2008): 586. http://dx.doi.org/10.1121/1.2857671.

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4

Page, Steven L. "Audio speaker system." Journal of the Acoustical Society of America 99, no. 3 (1996): 1277. http://dx.doi.org/10.1121/1.414786.

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5

Yagisawa, Toshihiro. "Audio mirror speaker." Journal of the Acoustical Society of America 100, no. 1 (1996): 23. http://dx.doi.org/10.1121/1.415929.

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6

Kery, Ervin, and Steve A. Alverson. "Audio speaker system." Journal of the Acoustical Society of America 91, no. 3 (1992): 1794. http://dx.doi.org/10.1121/1.403719.

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7

Minnerath, Donald L., and Robert J. Minnerath. "Audio speaker apparatus." Journal of the Acoustical Society of America 87, no. 2 (1990): 931. http://dx.doi.org/10.1121/1.398815.

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8

Babel, Molly. "Adaptation to Social-Linguistic Associations in Audio-Visual Speech." Brain Sciences 12, no. 7 (2022): 845. http://dx.doi.org/10.3390/brainsci12070845.

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Listeners entertain hypotheses about how social characteristics affect a speaker’s pronunciation. While some of these hypotheses may be representative of a demographic, thus facilitating spoken language processing, others may be erroneous stereotypes that impede comprehension. As a case in point, listeners’ stereotypes of language and ethnicity pairings in varieties of North American English can improve intelligibility and comprehension, or hinder these processes. Using audio-visual speech this study examines how listeners adapt to speech in noise from four speakers who are representative of s

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9

Ballesteros-Larrota, Dora Maria, Diego Renza-Torres, and Steven Andrés Camacho-Vargas. "Blind speaker identification for audio forensic purposes." DYNA 84, no. 201 (2017): 259. http://dx.doi.org/10.15446/dyna.v84n201.60407.

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Este artículo presenta un método ciego para identificación del hablante, con fines de audio forense. Se basa en un sistema de decisión que trabajo con reglas difusas y la correlación entre los cocleagramas del audio de prueba y de los audios de los sospechosos. Nuestro sistema proporciona salida nula, con único sospechoso o con un grupo de sospechosos. De acuerdo a las pruebas realizadas, el desempeño global del sistema (OA) es 0.97 con un valor de concordancia (índice kappa) de 0.75. Adicionalmente, a diferencia de sistemas clásicos en los que un bajo valor de selección incorrecta (FP) implic

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10

Hillerin, Marie Georgescu de. "Speaker Protocol." Consumer Electronics Test & Development 2021, no. 2 (2022): 56. http://dx.doi.org/10.12968/s2754-7744(23)70084-5.

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DXOMARK does not take audio lightly: the French quality evaluation expert built its own anechoic chamber, commissioned professional musicians, and even bought an apartment to use exclusively for its audio tests. But what exactly goes on behind the soundproof doors?

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Yaacoub Sahyoun, Joseph. "Low profile audio speaker." Journal of the Acoustical Society of America 118, no. 4 (2005): 2102. http://dx.doi.org/10.1121/1.2125192.

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12

Noro, Masao. "SPEAKER SYSTEM, AUDIO AMPLIFIER, AND AUDIO SYSTEM." Journal of the Acoustical Society of America 131, no. 5 (2012): 4220. http://dx.doi.org/10.1121/1.4712230.

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13

Khoma, Volodymyr, Yuriy Khoma, Vitalii Brydinskyi, and Alexander Konovalov. "Development of Supervised Speaker Diarization System Based on the PyAnnote Audio Processing Library." Sensors 23, no. 4 (2023): 2082. http://dx.doi.org/10.3390/s23042082.

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Diarization is an important task when work with audiodata is executed, as it provides a solution to the problem related to the need of dividing one analyzed call recording into several speech recordings, each of which belongs to one speaker. Diarization systems segment audio recordings by defining the time boundaries of utterances, and typically use unsupervised methods to group utterances belonging to individual speakers, but do not answer the question “who is speaking?” On the other hand, there are biometric systems that identify individuals on the basis of their voices, but such systems are

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14

Weychan, Radoslaw, Tomasz Marciniak, Agnieszka Stankiewicz, and Adam Dabrowski. "Real Time Recognition Of Speakers From Internet Audio Stream." Foundations of Computing and Decision Sciences 40, no. 3 (2015): 223–33. http://dx.doi.org/10.1515/fcds-2015-0014.

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Abstract In this paper we present an automatic speaker recognition technique with the use of the Internet radio lossy (encoded) speech signal streams. We show an influence of the audio encoder (e.g., bitrate) on the speaker model quality. The model of each speaker was calculated with the use of the Gaussian mixture model (GMM) approach. Both the speaker recognition and the further analysis were realized with the use of short utterances to facilitate real time processing. The neighborhoods of the speaker models were analyzed with the use of the ISOMAP algorithm. The experiments were based on fo

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Hustad, Katherine C., and Meghan A. Cahill. "Effects of Presentation Mode and Repeated Familiarization on Intelligibility of Dysarthric Speech." American Journal of Speech-Language Pathology 12, no. 2 (2003): 198–208. http://dx.doi.org/10.1044/1058-0360(2003/066).

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Clinical measures of speech intelligibility are widely used as one means of characterizing the speech of individuals with dysarthria. Many variables associated with both the speaker and the listener contribute to what is actually measured as intelligibility. The present study explored the effects of presentation modality (audiovisual vs. audio-only information) and the effects of speaker-specific familiarization across 4 trials on the intelligibility of speakers with mild and severe dysarthria associated with cerebral palsy. Results revealed that audiovisual information did not enhance intelli

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16

Vryzas, Nikolaos, Nikolaos Tsipas, and Charalampos Dimoulas. "Web Radio Automation for Audio Stream Management in the Era of Big Data." Information 11, no. 4 (2020): 205. http://dx.doi.org/10.3390/info11040205.

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Radio is evolving in a changing digital media ecosystem. Audio-on-demand has shaped the landscape of big unstructured audio data available online. In this paper, a framework for knowledge extraction is introduced, to improve discoverability and enrichment of the provided content. A web application for live radio production and streaming is developed. The application offers typical live mixing and broadcasting functionality, while performing real-time annotation as a background process by logging user operation events. For the needs of a typical radio station, a supervised speaker classificatio

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Rottenberg, William B., and Robert S. Robinson. "AUDIO SPEAKER WITH RADIAL ELECTROMAGNET." Journal of the Acoustical Society of America 132, no. 3 (2012): 1867. http://dx.doi.org/10.1121/1.4752132.

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18

Goldfarb, Barry S. "Audio bass speaker driver circuit." Journal of the Acoustical Society of America 103, no. 6 (1998): 3133. http://dx.doi.org/10.1121/1.423006.

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19

Spindler, William E. "Audio speaker with harmonic enclosure." Journal of the Acoustical Society of America 113, no. 2 (2003): 682. http://dx.doi.org/10.1121/1.1560236.

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20

V, Sethuram, Ande Prasad, and R. Rajeswara Rao. "Metaheuristic adapted convolutional neural network for Telugu speaker diarization." Intelligent Decision Technologies 15, no. 4 (2022): 561–77. http://dx.doi.org/10.3233/idt-211005.

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In speech technology, a pivotal role is being played by the Speaker diarization mechanism. In general, speaker diarization is the mechanism of partitioning the input audio stream into homogeneous segments based on the identity of the speakers. The automatic transcription readability can be improved with the speaker diarization as it is good in recognizing the audio stream into the speaker turn and often provides the true speaker identity. In this research work, a novel speaker diarization approach is introduced under three major phases: Feature Extraction, Speech Activity Detection (SAD), and

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21

Pejovic, Jovana, Eiling Yee, and Monika Molnar. "Speaker matters: Natural inter-speaker variation affects 4-month-olds’ perception of audio-visual speech." First Language 40, no. 2 (2019): 113–27. http://dx.doi.org/10.1177/0142723719876382.

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In the language development literature, studies often make inferences about infants’ speech perception abilities based on their responses to a single speaker. However, there can be significant natural variability across speakers in how speech is produced (i.e., inter-speaker differences). The current study examined whether inter-speaker differences can affect infants’ ability to detect a mismatch between the auditory and visual components of vowels. Using an eye-tracker, 4.5-month-old infants were tested on auditory-visual (AV) matching for two vowels (/i/ and /u/). Critically, infants were te

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22

Rokanatnam, Thurgeaswary, and Hazinah Kutty Mammi. "Study on Gender Identification Based on Audio Recordings Using Gaussian Mixture Model and Mel Frequency Cepstrum Coefficient Technique." International Journal of Innovative Computing 11, no. 2 (2021): 35–41. http://dx.doi.org/10.11113/ijic.v11n2.343.

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Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. The purpose of this study is to identify gender of speakers based on audio recordings. The objective of this study is to evaluate the accuracy rate of this technique to differentiate the gender and also to determine the performance rate to classify even when using self-acquired recordings. Audio forensics uses voice recordings as part of evidence to solve cases. This study is mainly conducted to provide an easier technique to identify the unknown speaker characteristics in forensic field. This e

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23

Wang, Suzhen, Lincheng Li, Yu Ding, and Xin Yu. "One-Shot Talking Face Generation from Single-Speaker Audio-Visual Correlation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 2531–39. http://dx.doi.org/10.1609/aaai.v36i3.20154.

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Audio-driven one-shot talking face generation methods are usually trained on video resources of various persons. However, their created videos often suffer unnatural mouth shapes and asynchronous lips because those methods struggle to learn a consistent speech style from different speakers. We observe that it would be much easier to learn a consistent speech style from a specific speaker, which leads to authentic mouth movements. Hence, we propose a novel one-shot talking face generation framework by exploring consistent correlations between audio and visual motions from a specific speaker and

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24

Chapple, Boo, and William Wong. "Can You Hear the Femur Play? Bone Audio Speakers at the Nanoscale." Leonardo 41, no. 4 (2008): 355–59. http://dx.doi.org/10.1162/leon.2008.41.4.355.

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This paper describes the research process involved in making audio speakers out of cow bone. The paper begins by discussing the conceptual basis of the work. It goes on to explain the piezoelectric nature of the bone matrix and how this makes it possible for bone to operate as an audio speaker. It then chronicles the process of working from a theoretical possibility to a functional speaker. In the concluding section of the paper, the final artifacts and conceptual outcomes of the process are discussed.

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25

Garcia, Jane Mertz, and Paul A. Dagenais. "Dysarthric Sentence Intelligibility." Journal of Speech, Language, and Hearing Research 41, no. 6 (1998): 1282–93. http://dx.doi.org/10.1044/jslhr.4106.1282.

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This study examined changes in the sentence intelligibility scores of speakers with dysarthria in association with different signal-independent factors (contextual influences). This investigation focused on the presence or absence of iconic gestures while speaking sentences with low or high semantic predictiveness. The speakers were 4 individuals with dysarthria, who varied from one another in terms of their level of speech intelligibility impairment, gestural abilities, and overall level of motor functioning. Ninety-six inexperienced listeners (24 assigned to each speaker) orthographically tr

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26

Prabhala, Jagat Chaitanya, Venkatnareshbabu K, and Ragoju Ravi. "OPTIMIZING SIMILARITY THRESHOLD FOR ABSTRACT SIMILARITY METRIC IN SPEECH DIARIZATION SYSTEMS: A MATHEMATICAL FORMULATION." Applied Mathematics and Sciences An International Journal (MathSJ) 10, no. 1/2 (2023): 1–10. http://dx.doi.org/10.5121/mathsj.2023.10201.

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Speaker diarization is a critical task in speech processing that aims to identify "who spoke when?" in an audio or video recording that contains unknown amounts of speech from unknown speakers and unknown number of speakers. Diarization has numerous applications in speech recognition, speaker identification, and automatic captioning. Supervised and unsupervised algorithms are used to address speaker diarization problems, but providing exhaustive labeling for the training dataset can become costly in supervised learning, while accuracy can be compromised when using unsupervised approaches. This

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27

Rakhmanenko, I. A., A. A. Shelupanov, and E. Y. Kostyuchenko. "Automatic text-independent speaker verification using convolutional deep belief network." Computer Optics 44, no. 4 (2020): 596–605. http://dx.doi.org/10.18287/2412-6179-co-621.

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This paper is devoted to the use of the convolutional deep belief network as a speech feature extractor for automatic text-independent speaker verification. The paper describes the scope and problems of automatic speaker verification systems. Types of modern speaker verification systems and types of speech features used in speaker verification systems are considered. The structure and learning algorithm of convolutional deep belief networks is described. The use of speech features extracted from three layers of a trained convolution deep belief network is proposed. Experimental studies of the

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28

Kasai, Junichi, Hiroshi Imai, and Takayuki Yanagishima. "Audio speaker system for automotive vehicle." Journal of the Acoustical Society of America 91, no. 3 (1992): 1796. http://dx.doi.org/10.1121/1.403705.

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29

Huang, Wan-Fang, and Chang Hua City. "Mult-channel audio center speaker device." Journal of the Acoustical Society of America 119, no. 2 (2006): 686. http://dx.doi.org/10.1121/1.2174496.

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30

Stiles, Enrique M., and Richard C. Calderwood. "Thermal chimney equipped audio speaker cabinet." Journal of the Acoustical Society of America 122, no. 2 (2007): 695. http://dx.doi.org/10.1121/1.2771300.

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31

Yanagishima, Takayuki. "Driver unit for automotive audio speaker." Journal of the Acoustical Society of America 80, no. 1 (1986): 371. http://dx.doi.org/10.1121/1.394076.

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32

Sunardi, Ariyawan, Aripin Triyanto, Nurkahfi Irwansyah, Woro Agus Nurtiyanto, Awalludin Saputra, and Muhammad Koiru Ikhsan. "PELATIHAN PEMASANGAN DAN PERAWATAN AUDIO SYSTEM DI MUSHOLA BAITURROHMAN, TAMBORA-JAKBAR." Jurnal Pengabdian Kepada Masyarakat (JPKM) - Aphelion 1, no. 01 (2020): 11. http://dx.doi.org/10.32493/jpka.v1i01.6901.

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Masjid dan musholla adalah tempat beribadah untuk umat islam. Peralatan pendukungnya antara lain audio system. Audio system digunakan untuk mengumandangkan adzan serta iqomah. Bagian pendukung dari peralatan audio system, antara lain adalah amplifier, speaker, microphone dan kabel. Amplifier berguna untuk mengatur suara yang keluar ke speaker. Amplifier dilengkapi dengan pengaturan keseimbangan suara, bass dan treble yang masing-masing digunakan untuk memperjelas suara. Speaker digunakan untuk audio system di dalam maupun luar mushola. Microphone digunakan untuk penghubung dari amplifier ke sp

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33

Zhang, Xu, and Liguo Weng. "Realistic Speech-Driven Talking Video Generation with Personalized Pose." Complexity 2020 (December 28, 2020): 1–8. http://dx.doi.org/10.1155/2020/6629634.

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In this work, we propose a method to transform a speaker’s speech information into a target character’s talking video; the method could make the mouth shape synchronization, expression, and body posture more realistic in the synthesized speaker video. This is a challenging task because changes of mouth shape and posture are coupled with audio semantic information. The model training is difficult to converge, and the model effect is unstable in complex scenes. Existing speech-driven speaker methods cannot solve this problem well. The method proposed in this paper first generates the sequence of

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34

Poojary, Nigam R., and K. H. Ashish. "Text To Speech with Custom Voice." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 4523–30. http://dx.doi.org/10.22214/ijraset.2023.51217.

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Abstract: The Text to Speech with Custom Voice system described in this work has vast applicability in numerous industries, including entertainment, education, and accessibility. The proposed text-to-speech (TTS) system is capable of generating speech audio in custom voices, even those not included in the training data. The system comprises a speaker encoder, a synthesizer, and a WaveRNN vocoder. Multiple speakers from a dataset of clean speech without transcripts are used to train the speaker encoder for a speaker verification process. The reference speech of the target speaker is used to cre

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35

Tsuchida, Masaru, Takahito Kawanishi, Hiroshi Murase, and Shigeru Takagi. "Joint Audio-Visual Tracking Based on Dynamically Weighted Linear Combination of Probability State Density." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 2 (2004): 190–99. http://dx.doi.org/10.20965/jaciii.2004.p0190.

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This paper proposes a method that can be applied to speaker tracking under stabilized, continuous conditions using visual and audio information even when input information is interrupted due to disturbance or occlusion caused by the effects of noise or varying illumination. Using this method, the position of a speaker is expressed based on a likelihood distribution that is obtained through integration of visual information and audio information. First, visual and audio information is integrated as as a weighted linear combination of probability density distribution, which is estimated as a res

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36

SUWANNATHAT, Thatsaphan, Jun-ichi IMAI, and Masahide KANEKO. "1P1-K06 Audio-Visual Speaker Detection in Human-Robot Interaction." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2007 (2007): _1P1—K06_1—_1P1—K06_4. http://dx.doi.org/10.1299/jsmermd.2007._1p1-k06_1.

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37

Wilcox, Lynn D. "Unsupervised speaker clustering for automatic speaker indexing of recorded audio data." Journal of the Acoustical Society of America 103, no. 4 (1998): 1701. http://dx.doi.org/10.1121/1.421064.

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38

Kimber, Donald G. "Method of speaker clustering for unknown speakers in conversation a audio data." Journal of the Acoustical Society of America 102, no. 5 (1997): 2480. http://dx.doi.org/10.1121/1.420370.

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39

Hustad, Katherine C., and Jane Mertz Garcia. "Aided and Unaided Speech Supplementation Strategies." Journal of Speech, Language, and Hearing Research 48, no. 5 (2005): 996–1012. http://dx.doi.org/10.1044/1092-4388(2005/068).

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Purpose: This study compared the influence of speaker-implemented iconic hand gestures and alphabet cues on speech intelligibility scores and strategy helpfulness ratings for 3 adults with cerebral palsy and dysarthria who differed from one another in their overall motor abilities. Method: A total of 144 listeners (48 per speaker) orthographically transcribed sentences spoken with alphabet cues (aided), iconic hand gestures (unaided), and a habitual speech control condition; scores were compared within audio-visual and audio-only listening formats. Results: When listeners were presented with s

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40

Singh, Satyanand. "Forensic and Automatic Speaker Recognition System." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 5 (2018): 2804. http://dx.doi.org/10.11591/ijece.v8i5.pp2804-2811.

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<span lang="EN-US">Current Automatic Speaker Recognition (ASR) System has emerged as an important medium of confirmation of identity in many businesses, ecommerce applications, forensics and law enforcement as well. Specialists trained in criminological recognition can play out this undertaking far superior by looking at an arrangement of acoustic, prosodic, and semantic attributes which has been referred to as structured listening. An algorithmbased system has been developed in the recognition of forensic speakers by physics scientists and forensic linguists to reduce the probability of

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41

Ahmad, Zubair, Alquhayz, and Ditta. "Multimodal Speaker Diarization Using a Pre-Trained Audio-Visual Synchronization Model." Sensors 19, no. 23 (2019): 5163. http://dx.doi.org/10.3390/s19235163.

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Speaker diarization systems aim to find ‘who spoke when?’ in multi-speaker recordings. The dataset usually consists of meetings, TV/talk shows, telephone and multi-party interaction recordings. In this paper, we propose a novel multimodal speaker diarization technique, which finds the active speaker through audio-visual synchronization model for diarization. A pre-trained audio-visual synchronization model is used to find the synchronization between a visible person and the respective audio. For that purpose, short video segments comprised of face-only regions are acquired using a face detecti

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42

Han, Cong, James O’Sullivan, Yi Luo, Jose Herrero, Ashesh D. Mehta, and Nima Mesgarani. "Speaker-independent auditory attention decoding without access to clean speech sources." Science Advances 5, no. 5 (2019): eaav6134. http://dx.doi.org/10.1126/sciadv.aav6134.

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Speech perception in crowded environments is challenging for hearing-impaired listeners. Assistive hearing devices cannot lower interfering speakers without knowing which speaker the listener is focusing on. One possible solution is auditory attention decoding in which the brainwaves of listeners are compared with sound sources to determine the attended source, which can then be amplified to facilitate hearing. In realistic situations, however, only mixed audio is available. We utilize a novel speech separation algorithm to automatically separate speakers in mixed audio, with no need for the s

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43

Dong, Yingjun, Neil G. MacLaren, Yiding Cao, et al. "Utterance Clustering Using Stereo Audio Channels." Computational Intelligence and Neuroscience 2021 (September 25, 2021): 1–8. http://dx.doi.org/10.1155/2021/6151651.

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Utterance clustering is one of the actively researched topics in audio signal processing and machine learning. This study aims to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. Processed audio signals were generated by combining left- and right-channel audio signals in a few different ways and then by extracting the embedded features (also called d-vectors) from those processed audio signals. This study applied the Gaussian mixture model for supervised utterance clustering. In the training phase, a parameter-sharing Gaussian mixture model was

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44

Jeyalakshmy, Mrs G. "Connection with the Multiple Bluetooth Speaker with the Single Device." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 558–62. http://dx.doi.org/10.22214/ijraset.2023.53442.

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Abstract: This paper presents the design and development of an Audio Connector application that enables users to route audio from a single input source to multiple output devices simultaneously. It is able to communicate with a wide range of devices without an interface. Users can concurrently stream audio to two wireless speakers or headphones using the Dual Audio functionality. Additionally, users can independently adjust the media output volume of each audio device. The times when you and your buddies would constantly quarrel about the volume of the audio are long gone. Theoretically, all B

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45

Linn, Aaron, and Leif Blackmon. "AUDIO SPEAKER HAVING A REMOVABLE VOICE COIL." Journal of the Acoustical Society of America 131, no. 3 (2012): 2343. http://dx.doi.org/10.1121/1.3696735.

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46

Harrison, Stanley N. "Speaker system with folded audio transmission passage." Journal of the Acoustical Society of America 90, no. 6 (1991): 3395. http://dx.doi.org/10.1121/1.401324.

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47

Stiles, Enrique M., and Richard C. Calderwood. "Audio speaker with graduated voice coil windings." Journal of the Acoustical Society of America 126, no. 1 (2009): 516. http://dx.doi.org/10.1121/1.3182960.

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48

Toh, Hilary, Pei Xuan Lee, Boon Pang Lim, and Nancy F. Chen. "Detecting speaker change in background audio streams." Journal of the Acoustical Society of America 134, no. 5 (2013): 4074. http://dx.doi.org/10.1121/1.4830881.

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49

Fan, Xing, and John H. L. Hansen. "Speaker Identification Within Whispered Speech Audio Streams." IEEE Transactions on Audio, Speech, and Language Processing 19, no. 5 (2011): 1408–21. http://dx.doi.org/10.1109/tasl.2010.2091631.

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50

Fedigan, Stephen John. "Apparatus And Method For Monitoring Speaker Cone Displacement In An Audio Speaker." Journal of the Acoustical Society of America 130, no. 6 (2011): 4175. http://dx.doi.org/10.1121/1.3668756.

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