Automatic Hypernasality Detection in Cleft Palate Speech Using CNN
暂无分享,去创建一个
Ming Tang | Heng Yin | Xiyue Wang | Ling He | Sen Yang | Hua Huang | H. Yin | Ling He | Sen Yang | Xiyue Wang | Mingxuan Tang | Hua Huang
[1] Daniel J. Singer. To the Best of Our Knowledge , 2021, The Philosophical Review.
[2] S. R. Mahadeva Prasanna,et al. Pitch-Adaptive Front-end Feature for Hypernasality Detection , 2018, INTERSPEECH.
[3] S. R. Mahadeva Prasanna,et al. Estimation of Hypernasality Scores from Cleft Lip and Palate Speech , 2018, INTERSPEECH.
[4] Bayya Yegnanarayana,et al. Discriminating Nasals and Approximants in English Language Using Zero Time Windowing , 2018, INTERSPEECH.
[5] S Dandapat,et al. Detection of hypernasality based on vowel space area. , 2018, The Journal of the Acoustical Society of America.
[6] Milos Cernak,et al. Nasal Speech Sounds Detection Using Connectionist Temporal Classification , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[7] Andrew P. Bradley,et al. Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning , 2017, IEEE Transactions on Medical Imaging.
[8] S. R. Mahadeva Prasanna,et al. Hypernasality Severity Analysis in Cleft Lip and Palate Speech Using Vowel Space Area , 2017, INTERSPEECH.
[9] Haytham M. Fayek,et al. Evaluating deep learning architectures for Speech Emotion Recognition , 2017, Neural Networks.
[10] Stanislas Chambon,et al. A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[11] Panying Rong,et al. Automatic identification of hypernasality in normal and cleft lip and palate patients with acoustic analysis of speech. , 2017, The Journal of the Acoustical Society of America.
[12] Md. Hanif Ali,et al. Cross-gender acoustic differences in hypernasal speech and detection of hypernasality , 2016, 2016 International Workshop on Computational Intelligence (IWCI).
[13] Balu Santhanam,et al. A joint EMD and Teager-Kaiser energy approach towards normal and nasal speech analysis , 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.
[14] Eric Granger,et al. Feature Learning from Spectrograms for Assessment of Personality Traits , 2016, IEEE Transactions on Affective Computing.
[15] Mansour Vali,et al. Detection of hypernasality from speech signal using group delay and wavelet transform , 2016, 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE).
[16] Xiuping Jia,et al. Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[17] S. Dandapat,et al. Zero time windowing analysis of hypernasality in speech of Cleft Lip and palate children , 2016, 2016 Twenty Second National Conference on Communication (NCC).
[18] Issue Information , 2016 .
[19] Bowen Zhou,et al. ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs , 2015, TACL.
[20] Zhang Wanli,et al. Application of Improved Spectral Subtraction Algorithm for Speech Emotion Recognition , 2015, 2015 IEEE Fifth International Conference on Big Data and Cloud Computing.
[21] Jesús Francisco Vargas-Bonilla,et al. Characterization Methods for the Detection of Multiple Voice Disorders: Neurological, Functional, and Laryngeal Diseases , 2015, IEEE Journal of Biomedical and Health Informatics.
[22] Christian Szegedy,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[23] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[24] Jorge Ivan Marin-Hurtado,et al. Pattern recognition of hypernasality in voice of patients with Cleft and Lip Palate , 2014, 2014 XIX Symposium on Image, Signal Processing and Artificial Vision.
[25] Jing Zhang,et al. Automatic Evaluation of Hypernasality and Consonant Misarticulation in Cleft Palate Speech , 2014, IEEE Signal Processing Letters.
[26] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[27] M. Bianchini,et al. On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[28] J. D. Arias-Londoño,et al. Nonlinear Dynamics for Hypernasality Detection in Spanish Vowels and Words , 2013, Cognitive Computation.
[29] A. Leonardis,et al. Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Elmar Nöth,et al. Automatic phoneme analysis in children with Cleft Lip and Palate , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[31] Lotfi Salhi,et al. Selection of pertinent acoustic features for detection of pathological voices , 2013, 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO).
[32] M. Vali,et al. Detection of hypernasal speech in children with cleft palate , 2012, 2012 19th Iranian Conference of Biomedical Engineering (ICBME).
[33] S. Jothilakshmi,et al. Segmentation of Continuous Speech into Consonant and Vowel Units using Formant Frequencies , 2012 .
[34] Emeka Nkenke,et al. Automatically evaluated degree of intelligibility of children with different cleft type from preschool and elementary school measured by automatic speech recognition. , 2012, International journal of pediatric otorhinolaryngology.
[35] Abhilasha Sharma,et al. Image understanding using decision tree based machine learning , 2011, ICIMU 2011 : Proceedings of the 5th international Conference on Information Technology & Multimedia.
[36] Yoshua Bengio,et al. Deep Sparse Rectifier Neural Networks , 2011, AISTATS.
[37] Jesús Francisco Vargas-Bonilla,et al. Automatic Detection of Hypernasality in Children , 2011, IWINAC.
[38] John H. L. Hansen,et al. A Review on Speech Recognition Technique , 2010 .
[39] Sheena Reilly,et al. A comparative study of two acoustic measures of hypernasality. , 2009, Journal of speech, language, and hearing research : JSLHR.
[40] Elmar Nöth,et al. Automatic detection of articulation disorders in children with cleft lip and palate. , 2009, The Journal of the Acoustical Society of America.
[41] T. Nagarajan,et al. Selective pole modification-based technique for the analysis and detection of hypernasality , 2009, TENCON 2009 - 2009 IEEE Region 10 Conference.
[42] J.C. Pereira,et al. Normal versus pathological voice signals , 2009, IEEE Engineering in Medicine and Biology Magazine.
[43] Kirill Sakhnov,et al. Pitch Detection Algorithms and Voiced/Unvoiced Classification for Noisy Speech , 2009, 2009 16th International Conference on Systems, Signals and Image Processing.
[44] S. Fu,et al. Evaluation of Hypernasality in Vowels Using Voice Low Tone to High Tone Ratio , 2009, The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association.
[45] Elmar Nöth,et al. Analysis of Hypernasal Speech in Children with Cleft Lip and Palate , 2008, TSD.
[46] David P Kuehn,et al. Universal Parameters for Reporting Speech Outcomes in Individuals with Cleft Palate , 2008, The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association.
[47] Tarun Pruthi,et al. Simulation and analysis of nasalized vowels based on magnetic resonance imaging data. , 2007, The Journal of the Acoustical Society of America.
[48] M. Ramasubba Reddy,et al. Acoustic Analysis and Detection of Hypernasality Using a Group Delay Function , 2007, IEEE Transactions on Biomedical Engineering.
[49] Elmar Nöth,et al. Intelligibility of Children with Cleft Lip and Palate: Evaluation by Speech Recognition Techniques , 2006, 18th International Conference on Pattern Recognition (ICPR'06).
[50] Terry B. J. Kuo,et al. Voice low tone to high tone ratio: a potential quantitative index for vowel [a:] and its nasalization , 2006, IEEE Transactions on Biomedical Engineering.
[51] T. Ananthakrishna,et al. k-means nearest neighbor classifier for voice pathology , 2004, Proceedings of the IEEE INDICON 2004. First India Annual Conference, 2004..
[52] Charles X. Ling,et al. AUC: A Better Measure than Accuracy in Comparing Learning Algorithms , 2003, Canadian Conference on AI.
[53] S. Li,et al. Analysis of speaker variability , 2001, INTERSPEECH.
[54] D. W. Warren,et al. The relationship between spectral characteristics and perceived hypernasality in children. , 2001, The Journal of the Acoustical Society of America.
[55] W. Fitch,et al. Morphology and development of the human vocal tract: a study using magnetic resonance imaging. , 1999, The Journal of the Acoustical Society of America.
[56] Shrikanth S. Narayanan,et al. Acoustics of children's speech: developmental changes of temporal and spectral parameters. , 1999, The Journal of the Acoustical Society of America.
[57] Y. Chen,et al. Formant frequency development: 15 to 36 months. , 1997, Journal of voice : official journal of the Voice Foundation.
[58] M. Kenney,et al. A longitudinal investigation of duration and temporal variability in children's speech production. , 1996, The Journal of the Acoustical Society of America.
[59] D P Kuehn,et al. Measurement of velopharyngeal closure force during vowel production. , 1994, The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association.
[60] Jerald B. Moon,et al. Measurement of Velopharyngeal Closure Force during Vowel Production , 1994 .
[61] J. Hillenbrand,et al. Acoustic characteristics of American English vowels. , 1994, The Journal of the Acoustical Society of America.
[62] John H. L. Hansen,et al. Discrete-Time Processing of Speech Signals , 1993 .
[63] D J Ostry,et al. Superior lateral pharyngeal wall movements in speech. , 1986, The Journal of the Acoustical Society of America.
[64] D B Pisoni,et al. Variability of Vowel Formant Frequencies and the Quantal Theory of Speech: A First Report , 1980, Phonetica.
[65] Ryan Wj,et al. Ultrasonic measurement of lateral pharyngeal wall movement at the velopharyngeal port. , 1976, The Cleft palate journal.
[66] L. Gerstman. Classification of self-normalized vowels , 1968 .
[67] Jing Zhang,et al. Automatic detection of glottal stop in cleft palate speech , 2018, Biomed. Signal Process. Control..
[68] Dessai Teja Deepak,et al. Spectral Analysis of Hypernasality in Cleft Palate Children: A Pre-Post Surgery Comparison. , 2016, Journal of clinical and diagnostic research : JCDR.
[69] A.K.M Fazlul Haque,et al. Variability of Acoustic Features of Hypernasality and it’s Assessment , 2016 .
[70] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[71] Geoffrey E. Hinton,et al. Deep Learning , 2015 .
[72] Dimitri Palaz,et al. Analysis of CNN-based speech recognition system using raw speech as input , 2015, INTERSPEECH.
[73] Wang Lirong,et al. Application of Improved Spectral Subtraction Algorithm for Speech Emotion Recognition , 2015, BDCloud.
[74] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[75] Geoffrey E. Hinton. A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.
[76] Academisch Proefschrift,et al. On variation and change in diphthongs and long vowels of spoken Dutch , 2009 .
[77] Yoshua Bengio. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[78] Tom Fawcett,et al. ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .
[79] Sundberg,et al. Music and Hearing Quarterly Progress and Status Report Effects of a velopharyngeal opening on the sound transfer characteristics of the vowel [ a ] , 2007 .
[81] G. Castellanos,et al. Acoustic Speech Analysis for Hypernasality Detection in Children , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.
[82] Chulhee Lee,et al. A Noninvasive Estimation of Hypernasality Using a Linear Predictive Model , 2004, Annals of Biomedical Engineering.
[83] S. Maeda,et al. Observations of velopharyngeal closure mechanism in horizontal and lateral direction from fiberscopic data , 2003 .
[84] Rajakrishnan Rajkumar,et al. Grammar Engineering for CCG using Ant and XSLT ∗ , 2001 .
[85] J.H.L. Hansen,et al. A noninvasive technique for detecting hypernasal speech using a nonlinear operator , 1996, IEEE Transactions on Biomedical Engineering.
[86] Shaun Wilson,et al. First report , 1992 .
[87] G. Henningsson,et al. Velopharyngeal movement patterns in patients alternating between oral and glottal articulation: a clinical and cineradiographical study. , 1986, The Cleft palate journal.