Automatic Hypernasality Detection in Cleft Palate Speech Using CNN

[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 .

[80]  Speech , Music and Hearing Quarterly Progress and Status Report Acoustic investigation of cleft palate speech before and after speech therapy , 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.