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Artículos de revistas sobre el tema "Perceptual features for speech recognition"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 1 de febrero de 2022

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1

Li, Guan Yu, Hong Zhi Yu, Yong Hong Li, and Ning Ma. "Features Extraction for Lhasa Tibetan Speech Recognition." Applied Mechanics and Materials 571-572 (June 2014): 205–8. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.205.

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Speech feature extraction is discussed. Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction coefficient (PLP) method is analyzed. These two types of features are extracted in Lhasa large vocabulary continuous speech recognition system. Then the recognition results are compared.

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2

Haque, Serajul, Roberto Togneri, and Anthony Zaknich. "Perceptual features for automatic speech recognition in noisy environments." Speech Communication 51, no. 1 (January 2009): 58–75. http://dx.doi.org/10.1016/j.specom.2008.06.002.

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3

Trabelsi, Imen, and Med Salim Bouhlel. "Comparison of Several Acoustic Modeling Techniques for Speech Emotion Recognition." International Journal of Synthetic Emotions 7, no. 1 (January 2016): 58–68. http://dx.doi.org/10.4018/ijse.2016010105.

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Automatic Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with a wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into different emotional states such as disgust, boredom, sadness, neutral, and happiness. The speech samples in this paper are from the Berlin emotional database. Mel Frequency cepstrum coefficients (MFCC), Linear prediction coefficients (LPC), linear prediction cepstrum coefficients (LPCC), Perceptual Linear Prediction (PLP) and Relative Spec

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4

Dua, Mohit, Rajesh Kumar Aggarwal, and Mantosh Biswas. "Optimizing Integrated Features for Hindi Automatic Speech Recognition System." Journal of Intelligent Systems 29, no. 1 (October 1, 2018): 959–76. http://dx.doi.org/10.1515/jisys-2018-0057.

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Abstract An automatic speech recognition (ASR) system translates spoken words or utterances (isolated, connected, continuous, and spontaneous) into text format. State-of-the-art ASR systems mainly use Mel frequency (MF) cepstral coefficient (MFCC), perceptual linear prediction (PLP), and Gammatone frequency (GF) cepstral coefficient (GFCC) for extracting features in the training phase of the ASR system. Initially, the paper proposes a sequential combination of all three feature extraction methods, taking two at a time. Six combinations, MF-PLP, PLP-MFCC, MF-GFCC, GF-MFCC, GF-PLP, and PLP-GFCC,

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5

Al Mahmud, Nahyan, and Shahfida Amjad Munni. "Qualitative Analysis of PLP in LSTM for Bangla Speech Recognition." International journal of Multimedia & Its Applications 12, no. 5 (October 30, 2020): 1–8. http://dx.doi.org/10.5121/ijma.2020.12501.

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The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual

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6

Kamińska, Dorota. "Emotional Speech Recognition Based on the Committee of Classifiers." Entropy 21, no. 10 (September 21, 2019): 920. http://dx.doi.org/10.3390/e21100920.

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This article presents the novel method for emotion recognition from speech based on committee of classifiers. Different classification methods were juxtaposed in order to compare several alternative approaches for final voting. The research is conducted on three different types of Polish emotional speech: acted out with the same content, acted out with different content, and spontaneous. A pool of descriptors, commonly utilized for emotional speech recognition, expanded with sets of various perceptual coefficients, is used as input features. This research shows that presented approach improve

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7

Dmitrieva, E., V. Gelman, K. Zaitseva, and A. Orlov. "Psychophysiological features of perceptual learning in the process of speech emotional prosody recognition." International Journal of Psychophysiology 85, no. 3 (September 2012): 375. http://dx.doi.org/10.1016/j.ijpsycho.2012.07.034.

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8

Seyedin, Sanaz, Seyed Mohammad Ahadi, and Saeed Gazor. "New Features Using Robust MVDR Spectrum of Filtered Autocorrelation Sequence for Robust Speech Recognition." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/634160.

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This paper presents a novel noise-robust feature extraction method for speech recognition using the robust perceptual minimum variance distortionless response (MVDR) spectrum of temporally filtered autocorrelation sequence. The perceptual MVDR spectrum of the filtered short-time autocorrelation sequence can reduce the effects of residue of the nonstationary additive noise which remains after filtering the autocorrelation. To achieve a more robust front-end, we also modify the robust distortionless constraint of the MVDR spectral estimation method via revised weighting of the subband power spec

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9

Kaur, Gurpreet, Mohit Srivastava, and Amod Kumar. "Genetic Algorithm for Combined Speaker and Speech Recognition using Deep Neural Networks." Journal of Telecommunications and Information Technology 2 (June 29, 2018): 23–31. http://dx.doi.org/10.26636/jtit.2018.119617.

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Huge growth is observed in the speech and speaker recognition ﬁeld due to many artiﬁcial intelligence algorithms being applied. Speech is used to convey messages via the language being spoken, emotions, gender and speaker identity. Many real applications in healthcare are based upon speech and speaker recognition, e.g. a voice-controlled wheelchair helps control the chair. In this paper, we use a genetic algorithm (GA) for combined speaker and speech recognition, relying on optimized Mel Frequency Cepstral Coeﬃcient (MFCC) speech features, and classiﬁcation is performed using a Deep Neural Net

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10

Trabelsi, Imen, and Med Salim Bouhlel. "Feature Selection for GUMI Kernel-Based SVM in Speech Emotion Recognition." International Journal of Synthetic Emotions 6, no. 2 (July 2015): 57–68. http://dx.doi.org/10.4018/ijse.2015070104.

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Speech emotion recognition is the indispensable requirement for efficient human machine interaction. Most modern automatic speech emotion recognition systems use Gaussian mixture models (GMM) and Support Vector Machines (SVM). GMM are known for their performance and scalability in the spectral modeling while SVM are known for their discriminatory power. A GMM-supervector characterizes an emotional style by the GMM parameters (mean vectors, covariance matrices, and mixture weights). GMM-supervector SVM benefits from both GMM and SVM frameworks. In this paper, the GMM-UBM mean interval (GUMI) ke

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11

Lalitha, S., and Deepa Gupta. "An Encapsulation of Vital Non-Linear Frequency Features for Various Speech Applications." Journal of Computational and Theoretical Nanoscience 17, no. 1 (January 1, 2020): 303–7. http://dx.doi.org/10.1166/jctn.2020.8666.

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Mel Frequency Cepstral Coefficients (MFCCs) and Perceptual linear prediction coefficients (PLPCs) are widely casted nonlinear vocal parameters in majority of the speaker identification, speaker and speech recognition techniques as well in the field of emotion recognition. Post 1980s, significant exertions are put forth on for the progress of these features. Considerations like the usage of appropriate frequency estimation approaches, proposal of appropriate filter banks, and selection of preferred features perform a vital part for the strength of models employing these features. This article p

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12

Linkai Bu and T. D. Church. "Perceptual speech processing and phonetic feature mapping for robust vowel recognition." IEEE Transactions on Speech and Audio Processing 8, no. 2 (March 2000): 105–14. http://dx.doi.org/10.1109/89.824695.

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13

Helali, W., Ζ. Hajaiej, and A. Cherif. "Real Time Speech Recognition based on PWP Thresholding and MFCC using SVM." Engineering, Technology & Applied Science Research 10, no. 5 (October 26, 2020): 6204–8. http://dx.doi.org/10.48084/etasr.3759.

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The real-time performance of Automatic Speech Recognition (ASR) is a big challenge and needs high computing capability and exhaustive memory consumption. Getting a robust performance against inevitable various difficult situations such as speaker variations, accents, and noise is a tedious task. It’s crucial to expand new and efficient approaches for speech signal extraction features and pre-processing. In order to fix the high dependency issue related to processing succeeding steps in ARS and enhance the extracted features’ quality, noise robustness can be solved within the ARS extraction blo

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14

Burgos, Pepi, Roeland van Hout, and Brigitte Planken. "Matching Acoustical Properties and Native Perceptual Assessments of L2 Speech." Open Linguistics 4, no. 1 (January 1, 2018): 199–226. http://dx.doi.org/10.1515/opli-2018-0011.

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AbstractThis article analyses the acoustical properties of Dutch vowels produced by adult Spanish learners and investigates how these vowels are perceived by non-expert native Dutch listeners. Statistical vowel classifications obtained from the acoustical properties of the learner vowel realizations were compared to vowel classifications provided by native Dutch listeners. Both types of classifications were affected by the specific set of vowels included as stimuli, an effect caused by the large variability in Spanish learners’ vowel realizations. While there were matches between the two types

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15

Smith, Kimberly G., and Daniel Fogerty. "Integration of Partial Information Within and Across Modalities: Contributions to Spoken and Written Sentence Recognition." Journal of Speech, Language, and Hearing Research 58, no. 6 (December 2015): 1805–17. http://dx.doi.org/10.1044/2015_jslhr-h-14-0272.

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PurposeThis study evaluated the extent to which partial spoken or written information facilitates sentence recognition under degraded unimodal and multimodal conditions.MethodTwenty young adults with typical hearing completed sentence recognition tasks in unimodal and multimodal conditions across 3 proportions of preservation. In the unimodal condition, performance was examined when only interrupted text or interrupted speech stimuli were available. In the multimodal condition, performance was examined when both interrupted text and interrupted speech stimuli were concurrently presented. Sente

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16

CAI, Shang, Yeming XIAO, Jielin PAN, Qingwei ZHAO, and Yonghong YAN. "Noise Robust Feature Scheme for Automatic Speech Recognition Based on Auditory Perceptual Mechanisms." IEICE Transactions on Information and Systems E95.D, no. 6 (2012): 1610–18. http://dx.doi.org/10.1587/transinf.e95.d.1610.

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17

Nashipudimath, Madhu M., Pooja Pillai, Anupama Subramanian, Vani Nair, and Sarah Khalife. "Voice Feature Extraction for Gender and Emotion Recognition." ITM Web of Conferences 40 (2021): 03008. http://dx.doi.org/10.1051/itmconf/20214003008.

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Voice recognition plays a key function in spoken communication that facilitates identifying the emotions of a person that reflects within the voice. Gender classification through speech is a popular Human Computer Interaction (HCI) method on account that determining gender through computer is hard. This led to the development of a model for "Voice feature extraction for Emotion and Gender Recognition". The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have specific vocal traits because of their acoust

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18

Nair, Vani, Pooja Pillai, Anupama Subramanian, Sarah Khalife, and Dr Madhu Nashipudimath. "Voice Feature Extraction for Gender and Emotion Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 9, no. 5 (May 31, 2021): 17–22. http://dx.doi.org/10.17762/ijritcc.v9i5.5463.

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Voice recognition plays a key role in spoken communication that helps to identify the emotions of a person that reflects in the voice. Gender classification through speech is a widely used Human Computer Interaction (HCI) as it is not easy to identify gender by computer. This led to the development of a model for “Voice feature extraction for Emotion and Gender Recognition”. The speech signal consists of semantic information, speaker information (gender, age, emotional state), accompanied by noise. Females and males have different voice characteristics due to their acoustical and perceptual di

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19

Davies-Venn, Evelyn, and Pamela Souza. "The Role of Spectral Resolution, Working Memory, and Audibility in Explaining Variance in Susceptibility to Temporal Envelope Distortion." Journal of the American Academy of Audiology 25, no. 06 (June 2014): 592–604. http://dx.doi.org/10.3766/jaaa.25.6.9.

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Background: Several studies have shown that hearing thresholds alone cannot adequately predict listeners’ success with hearing-aid amplification. Furthermore, previous studies have shown marked differences in listeners’ susceptibility to distortions introduced by certain nonlinear amplification parameters. Purpose: The purpose of this study was to examine the role of spectral resolution, working memory, and audibility in explaining perceptual susceptibility to temporal envelope and other hearing-aid compression-induced distortions for listeners with mild to moderate and moderate to severe hear

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20

Cabral, Frederico Soares, Hidekazu Fukai, and Satoshi Tamura. "Feature Extraction Methods Proposed for Speech Recognition Are Effective on Road Condition Monitoring Using Smartphone Inertial Sensors." Sensors 19, no. 16 (August 9, 2019): 3481. http://dx.doi.org/10.3390/s19163481.

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The objective of our project is to develop an automatic survey system for road condition monitoring using smartphone devices. One of the main tasks of our project is the classification of paved and unpaved roads. Assuming recordings will be archived by using various types of vehicle suspension system and speeds in practice, hence, we use the multiple sensors found in smartphones and state-of-the-art machine learning techniques for signal processing. Despite usually not being paid much attention, the results of the classification are dependent on the feature extraction step. Therefore, we have

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21

Massaro, Dominic W. "Multiple Book Review of Speech perception by ear and eye: A paradigm for psychological inquiry." Behavioral and Brain Sciences 12, no. 4 (December 1989): 741–55. http://dx.doi.org/10.1017/s0140525x00025619.

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AbstractThis book is about the processing of information in face-to-face communication when a speaker makes both audible and visible information available to a perceiver. Both auditory and visual sources of information are evaluated and integrated to achieve speech perception. The evaluation of the information source provides information about the strength of alternative interpretations, rather than just all-or-none categorical information, as claimed by “categorical perception” theory. Information sources are evaluated independently; the integration process insures that the least ambiguous so

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22

Dua, Mohit, Rajesh Kumar Aggarwal, and Mantosh Biswas. "Discriminative Training Using Noise Robust Integrated Features and Refined HMM Modeling." Journal of Intelligent Systems 29, no. 1 (February 20, 2018): 327–44. http://dx.doi.org/10.1515/jisys-2017-0618.

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Abstract The classical approach to build an automatic speech recognition (ASR) system uses different feature extraction methods at the front end and various parameter classification techniques at the back end. The Mel-frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) techniques are the conventional approaches used for many years for feature extraction, and the hidden Markov model (HMM) has been the most obvious selection for feature classification. However, the performance of MFCC-HMM and PLP-HMM-based ASR system degrades in real-time environments. The proposed work

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23

Schädler, Marc R., David Hülsmeier, Anna Warzybok, and Birger Kollmeier. "Individual Aided Speech-Recognition Performance and Predictions of Benefit for Listeners With Impaired Hearing Employing FADE." Trends in Hearing 24 (January 2020): 233121652093892. http://dx.doi.org/10.1177/2331216520938929.

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The benefit in speech-recognition performance due to the compensation of a hearing loss can vary between listeners, even if unaided performance and hearing thresholds are similar. To accurately predict the individual performance benefit due to a specific hearing device, a prediction model is proposed which takes into account hearing thresholds and a frequency-dependent suprathreshold component of impaired hearing. To test the model, the German matrix sentence test was performed in unaided and individually aided conditions in quiet and in noise by 18 listeners with different degrees of hearing

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24

Myronova, T. Yu, and O. V. Kovalevska. "Methods of development orientational skills in a foreign text." Bulletin of Luhansk Taras Shevchenko National University, no. 4 (335) (2020): 195–202. http://dx.doi.org/10.12958/2227-2844-2020-4(335)-195-202.

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The article is devoted to the implementation of the methodical approach as teaching reading in foreign language to students of non-philological specialties on the basis of specific language material. It is based on the essential characteristics of reading as a type of speech activity based on the analysis of grammatical features contained in the text. The approach of teaching reading covered in the article involves managing the process of development an indicative basis for educational activities. This method has great advantages, because it helps to develop skills of creative analysis of the

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Bach, Jörg-Hendrik, Jörn Anemüller, and Birger Kollmeier. "Robust speech detection in real acoustic backgrounds with perceptually motivated features." Speech Communication 53, no. 5 (May 2011): 690–706. http://dx.doi.org/10.1016/j.specom.2010.07.003.

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Wolfe, Jace, Mila Duke, Erin Schafer, Christine Jones, and Lori Rakita. "Evaluation of Adaptive Noise Management Technologies for School-Age Children with Hearing Loss." Journal of the American Academy of Audiology 28, no. 05 (May 2017): 415–35. http://dx.doi.org/10.3766/jaaa.16015.

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Background: Children with hearing loss experience significant difficulty understanding speech in noisy and reverberant situations. Adaptive noise management technologies, such as fully adaptive directional microphones and digital noise reduction, have the potential to improve communication in noise for children with hearing aids. However, there are no published studies evaluating the potential benefits children receive from the use of adaptive noise management technologies in simulated real-world environments as well as in daily situations. Purpose: The objective of this study was to compare s

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27

Abid Noor, Ali O. "Robust speaker verification in band-localized noise conditions." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 2 (February 1, 2019): 499. http://dx.doi.org/10.11591/ijeecs.v13.i2.pp499-506.

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This research paper presents a robust method for speaker verification in noisy environments. The noise is assumed to contaminate certain parts of the voice’s frequency spectrum. Therefore, the verification method is based on splitting the noisy speech into subsidiary bands then using a threshold to sense the existence of noise in a specific part of the spectrum, hence activating an adaptive filter in that part to track changes in noise’s characteristics and remove it. The decomposition is achieved using low complexity quadrature mirror filters QMF in three levels thus achieving four bands in a

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Kwak, Yuna, Hosung Nam, Hyun-Woong Kim, and Chai-Youn Kim. "Cross-Modal Correspondence Between Speech Sound and Visual Shape Influencing Perceptual Representation of Shape: the Role of Articulation and Pitch." Multisensory Research 33, no. 6 (June 17, 2020): 569–98. http://dx.doi.org/10.1163/22134808-20191330.

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Abstract Cross-modal correspondence is the tendency to systematically map stimulus features across sensory modalities. The current study explored cross-modal correspondence between speech sound and shape (Experiment 1), and whether such association can influence shape representation (Experiment 2). For the purpose of closely examining the role of the two factors — articulation and pitch — combined in speech acoustics, we generated two sets of 25 vowel stimuli — pitch-varying and pitch-constant sets. Both sets were generated by manipulating articulation — frontness and height of the tongue body

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Frey, Brendan J., and Geoffrey E. Hinton. "Variational Learning in Nonlinear Gaussian Belief Networks." Neural Computation 11, no. 1 (January 1, 1999): 193–213. http://dx.doi.org/10.1162/089976699300016872.

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We view perceptual tasks such as vision and speech recognition as inference problems where the goal is to estimate the posterior distribution over latent variables (e.g., depth in stereo vision) given the sensory input. The recent flurry of research in independent component analysis exemplifies the importance of inferring the continuous-valued latent variables of input data. The latent variables found by this method are linearly related to the input, but perception requires nonlinear inferences such as classification and depth estimation. In this article, we present a unifying framework for st

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Gfeller, Kate, Dingfeng Jiang, Jacob J. Oleson, Virginia Driscoll, and John F. Knutson. "Temporal Stability of Music Perception and Appraisal Scores of Adult Cochlear Implant Recipients." Journal of the American Academy of Audiology 21, no. 01 (January 2010): 028–34. http://dx.doi.org/10.3766/jaaa.21.1.4.

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Background: An extensive body of literature indicates that cochlear implants (CIs) are effective in supporting speech perception of persons with severe to profound hearing losses who do not benefit to any great extent from conventional hearing aids. Adult CI recipients tend to show significant improvement in speech perception within 3 mo following implantation as a result of mere experience. Furthermore, CI recipients continue to show modest improvement as long as 5 yr postimplantation. In contrast, data taken from single testing protocols of music perception and appraisal indicate that CIs ar

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31

Sheldon, Claire A., George L. Malcolm, and Jason J. S. Barton. "Alexia With and Without Agraphia: An Assessment of Two Classical Syndromes." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 35, no. 5 (November 2008): 616–24. http://dx.doi.org/10.1017/s0317167100009410.

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Background:Current cognitive models propose that multiple processes are involved in reading and writing.Objective:Our goal was to use linguistic analyses to clarify the cognitive dysfunction behind two classic alexic syndromes.Methods:We report four experiments on two patients, one with alexia without agraphia following occipitotemporal lesions, and one with alexia with agraphia from a left angular gyral lesion.Results:The patient with occipital lesions had trouble discriminating real letters from foils and his reading varied with word-length but not with linguistic variables such as part of s

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Nusbaum, Howard C. "Perceptual expectations, attention, and speech recognition." Journal of the Acoustical Society of America 127, no. 3 (March 2010): 1890. http://dx.doi.org/10.1121/1.3384714.

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Aliūkaitė, Daiva, and Danguolė Mikulėnienė. "The narrative of an ordinary member of language community: WHERE and WHY is dialecticity of a locality created." Lietuvių kalba, no. 13 (December 20, 2019): 1–22. http://dx.doi.org/10.15388/lk.2019.22481.

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The paper aims to explore where and why an ordinary member of language community creates the dialecticity of a locality and evaluate whether (and how) the dialect artefact of an ordinary member of language community is related with the dialecticity recognised and estimated by researchers, or, in other words, discuss the interaction of the emic and etic perspectives.The empirical basis for the discussion about the interaction of the emic and etic perspectives is formed on the verbalised and visualised language attitudes of the ordinary members of language community and the data of the text-stim

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Zhao, Kai, and Dan Wang. "Research on Speech Recognition Method in Multi Layer Perceptual Network Environment." International Journal of Circuits, Systems and Signal Processing 15 (August 24, 2021): 996–1004. http://dx.doi.org/10.46300/9106.2021.15.107.

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Aiming at the problem of low recognition rate in speech recognition methods, a speech recognition method in multi-layer perceptual network environment is proposed. In the multi-layer perceptual network environment, the speech signal is processed in the filter by using the transfer function of the filter. According to the framing process, the speech signal is windowed and framing processed to remove the silence segment of the speech signal. At the same time, the average energy of the speech signal is calculated and the zero crossing rate is calculated to extract the characteristics of the speec

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Axelrod, Scott E. "Speech recognition utilizing multitude of speech features." Journal of the Acoustical Society of America 128, no. 4 (2010): 2259. http://dx.doi.org/10.1121/1.3500788.

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Allen, Jont B., and Marion Regnier. "SPEECH AND METHOD FOR IDENTIFYING PERCEPTUAL FEATURES." Journal of the Acoustical Society of America 132, no. 4 (2012): 2779. http://dx.doi.org/10.1121/1.4757834.

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Mattys, Sven L., and Shekeila D. Palmer. "Divided attention disrupts perceptual encoding during speech recognition." Journal of the Acoustical Society of America 137, no. 3 (March 2015): 1464–72. http://dx.doi.org/10.1121/1.4913507.

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Eide, Ellen M. "Speech recognition using discriminant features." Journal of the Acoustical Society of America 126, no. 3 (2009): 1646. http://dx.doi.org/10.1121/1.3230471.

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Huang, Chang-Han, and Frank Torsten Bernd Seide. "Tone features for speech recognition." Journal of the Acoustical Society of America 117, no. 5 (2005): 2698. http://dx.doi.org/10.1121/1.1932393.

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Bahl, Lalit R. "Speech recognition using dynamic features." Journal of the Acoustical Society of America 102, no. 6 (1997): 3252. http://dx.doi.org/10.1121/1.420242.

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Shafiro, Valeriy, Daniel Fogerty, Kimberly Smith, and Stanley Sheft. "Perceptual Organization of Interrupted Speech and Text." Journal of Speech, Language, and Hearing Research 61, no. 10 (October 26, 2018): 2578–88. http://dx.doi.org/10.1044/2018_jslhr-h-17-0477.

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Purpose Visual recognition of interrupted text may predict speech intelligibility under adverse listening conditions. This study investigated the nature of the linguistic information and perceptual processes underlying this relationship. Method To directly compare the perceptual organization of interrupted speech and text, we examined the recognition of spoken and printed sentences interrupted at different rates in 14 adults with normal hearing. The interruption method approximated deletion and retention of rate-specific linguistic information (0.5–64 Hz) in speech by substituting either white

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42

Jones, Harrison N., Kelly D. Crisp, Maragatha Kuchibhatla, Leslie Mahler, Thomas Risoli, Carlee W. Jones, and Priya Kishnani. "Auditory-Perceptual Speech Features in Children With Down Syndrome." American Journal on Intellectual and Developmental Disabilities 124, no. 4 (July 1, 2019): 324–38. http://dx.doi.org/10.1352/1944-7558-124.4.324.

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Abstract Speech disorders occur commonly in individuals with Down syndrome (DS), although data regarding the auditory-perceptual speech features are limited. This descriptive study assessed 47 perceptual speech features during connected speech samples in 26 children with DS. The most severely affected speech features were: naturalness, imprecise consonants, hyponasality, speech rate, inappropriate silences, irregular vowels, prolonged intervals, overall loudness level, pitch level, aberrant oropharyngeal resonance, hoarse voice, reduced stress, and prolonged phonemes. These findings suggest th

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43

Richter, Caitlin, Naomi H. Feldman, Harini Salgado, and Aren Jansen. "Evaluating Low-Level Speech Features Against Human Perceptual Data." Transactions of the Association for Computational Linguistics 5 (December 2017): 425–40. http://dx.doi.org/10.1162/tacl_a_00071.

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We introduce a method for measuring the correspondence between low-level speech features and human perception, using a cognitive model of speech perception implemented directly on speech recordings. We evaluate two speaker normalization techniques using this method and find that in both cases, speech features that are normalized across speakers predict human data better than unnormalized speech features, consistent with previous research. Results further reveal differences across normalization methods in how well each predicts human data. This work provides a new framework for evaluating low-l

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44

Small, Larry H. "Listeners' Perceptual Strategies in Word Recognition: Shadowing Misarticulated Speech." Perceptual and Motor Skills 69, no. 3_suppl (December 1989): 1211–16. http://dx.doi.org/10.2466/pms.1989.69.3f.1211.

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The purpose of the study was to examine the perceptual salience of various types of phonetic, lexical, and prosodic information by examining subjects' responses to altered words in a continuous speech-shadowing task. 48 subjects shadowed a prose passage in which the word initial consonant of 14 two-syllable words was altered by either mispronouncing or deleting it. Analysis of responses showed that subjects made use of lexical stress and stressed vowel information during word recognition to cope with the altered auditory signal

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45

Small, Larry H. "Listeners’ Perceptual Strategies in Word Recognition: Shadowing Misarticulated Speech." Perceptual and Motor Skills 69, no. 3-2 (December 1989): 1211–16. http://dx.doi.org/10.1177/00315125890693-226.

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The purpose of the study was to examine the perceptual salience of various types of phonetic, lexical, and prosodic information by examining subjects’ responses to altered words in a continuous speech-shadowing task. 48 subjects shadowed a prose passage in which the word initial consonant of 14 two-syllable words was altered by either mispronouncing or deleting it. Analysis of responses showed that subjects made use of lexical stress and stressed vowel information during word recognition to cope with the altered auditory signal.

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46

Nusbaum, Howard. "Perceptual learning and expectations: Cognitive mechanisms in speech recognition." Journal of the Acoustical Society of America 125, no. 4 (April 2009): 2604. http://dx.doi.org/10.1121/1.4783910.

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47

Thomas-Stonell, Nancy, Ava-Lee Kotler, Herbert Leeper, and Philip Doyle. "Computerized speech recognition: influence of intelligibility and perceptual consistency on recognition accuracy." Augmentative and Alternative Communication 14, no. 1 (January 1998): 51–56. http://dx.doi.org/10.1080/07434619812331278196.

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48

Najnin, Shamima, and Bonny Banerjee. "Speech recognition using cepstral articulatory features." Speech Communication 107 (February 2019): 26–37. http://dx.doi.org/10.1016/j.specom.2019.01.002.

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49

Potamianos, Alexandros. "Novel features for robust speech recognition." Journal of the Acoustical Society of America 112, no. 5 (November 2002): 2278. http://dx.doi.org/10.1121/1.4779131.

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50

Lee, Youngjik Lee, and Kyu-Woong Hwang Hwang. "Selecting Good Speech Features for Recognition." ETRI Journal 18, no. 1 (April 1, 1996): 29–40. http://dx.doi.org/10.4218/etrij.96.0196.0013.

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