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Automatic detection of speech disorders with the use of Hidden Markov Model

Marek Wisniewski, Wieslawa Kuniszyk-Józkowiak, Elzbieta Smolka, Waldemar Suszynski
2007 Annales UMCS Informatica  
The most frequently used methods of automatic detection and classification of speech disorders are based on experimental determination of specific distinctive features for a given kind of disorder, and  ...  Therefore, for every kind of a disorder, a separate algorithm needs to be worked out. Another, more flexible approach is the application of the Hidden Markov Models (HMM).  ...  Acknowledgements Scientific work partially financed from the grant of Vice-Rector of Maria Curie-Sk odowska Univeristy. The authors thank Natalia Fedan for language corrections. Pobrane z czasopisma  ... 
dblp:journals/umcs/WisniewskiKSS07 fatcat:g4kpe544qfda3lk3f6ykmsexre

A Review on Detection of Nasalized Voiced Stops in Cleft Palate Speech

Jihan N. K, Sreevarsha. V
2020 Zenodo  
The detection of nasalized voice stops is considered in important during the diagnosis and speech therapy of individuals with CP.  ...  There are several methods used for the detection of nasalized voice stops.  ...  Detection Based on Hidden Markov Model Hidden markov model based force-alignment method is used in [4] for the segmentation of consonant production errors.  ... 
doi:10.5281/zenodo.3892366 fatcat:2lelbd7ipzht5p4w2wsp7k4yru

Speech Recognition with Hidden Markov Model and Multisensory Methods in Learning Applications Intended to Help Dyslexic Children

B Siregar, AJ Tarigan, S Nasution, U Andayani, F Fahmi
2019 Journal of Physics, Conference Series  
This study utilizes the hidden Markov method for speech recognition and multisensory methods for learning media.  ...  Each level uses a pattern that is generally difficult to accept children with dyslexia disorders.  ...  The algorithm used for speech recognition is the Hidden Markov Model, while for learning is a multisensory method.  ... 
doi:10.1088/1742-6596/1235/1/012051 fatcat:d5h36cxeuvdzfgr254q6r47e6a

Automatic detection of interaction groups

Oliver Brdiczka, Jérôme Maisonnasse, Patrick Reignier
2005 Proceedings of the 7th international conference on Multimodal interfaces - ICMI '05  
Our approach takes speech activity detection of individuals forming interaction groups as input.  ...  In this regard, the dynamic change of interaction group configuration, i.e. the split and merge of interaction groups, can be seen as indicator of new activities.  ...  Each a priori group configuration class is associated with a Hidden Markov Model.  ... 
doi:10.1145/1088463.1088473 dblp:conf/icmi/BrdiczkaMR05 fatcat:erwxux264relzcen2sgeaoxd54

Automatic Assessment of Pronunciation Quality of Children within Assisted Speech Therapy

O. A. Schipor, S. G. Pentiuc, M. D. Schipor
2012 Elektronika ir Elektrotechnika  
We present both theoretical and practical related issues such as: acquisition of data, human scoring, Hidden Markov Models training and classification, and the performances of our system.  ...  The obtained results encourage us to continue the development of Logomon -the first computer based speech therapy system for Romanian language.  ...  Conclusions In this paper we focus on human and automatic scoring of pronunciation of children with speech disorders.  ... 
doi:10.5755/j01.eee.122.6.1813 fatcat:ny4fificwjbp5mpcfm4ika3qvi

Automatic Speech Recognition of Pathological Voice

Algabri Mohammed, Alsulaiman Mansour, Muhammad Ghulam, Zakariah Mohammed, Tamer A. Mesallam, Khalid H. Malki, Farahat Mohamed, M. A. Mekhtiche, Bencherif Mohamed
2015 Indian Journal of Science and Technology  
Methods: In this paper, we proposed an automatic speech recognition system using Hidden Markov Model Toolkit (HTK) for normal and pathology voice.  ...  The database that used to evaluate the performance of the developed system; includes a total of 297 speakers 121 of them were normal speakers and the remaining containing five types of vocal fold disorders  ...  The system was developed using Hidden Markov Model (HMM) which lead to the development of highly reliable system to recognize the speech.  ... 
doi:10.17485/ijst/2015/v8i32/92130 fatcat:6pbskygbtfhalilo24kgmplg5u

Automatic Detection of Disorders in a Continuous Speech with the Hidden Markov Models Approach [chapter]

Marek Wiśniewski, Wieslawa Kuniszyk-Jóźkowiak, Elzbieta Smołka, Waldemar Suszyński
2007 Advances in Soft Computing  
The used algorithms were briefly described and the final method of speech disorders detection was presented.  ...  In the work algorithms commonly utilized in continuous speech recognition systems were applied to detection of speech disorders.  ...  The natural way of gaining information about phonemes in speech is utilization of the Hidden Markov Models and proper algorithms commonly used in speech recognition systems.  ... 
doi:10.1007/978-3-540-75175-5_56 dblp:series/asc/WisniewskiKSS08 fatcat:mm2wjlvg45ec5jvfdyq4jjqotu

A HTK-based Method for Detecting Vocal Fold Pathology

Vahid Majidnezhad
2014 Acta Informatica Medica  
Methods: In this work, a method based on the Hidden Markov model Toolkit (HTK) for detecting vocal fold pathology in the Russian digits is developed which belongs to the second category.  ...  It employs a three state HMM for modeling each phoneme. Results: According to the results of the experiments, the proposed method achieves the 90% of detection accuracy.  ...  There are many works in the first category. For example, in (2) vocal fold pathology was detected using Hidden Markov Model (HMM).  ... 
doi:10.5455/aim.2014.22.246-248 pmid:25395726 pmcid:PMC4216426 fatcat:dnhaebxvrbhdzkbjn6xgxx3gai

Audio-Visual Speech Recognition for People with Speech Disorders

Elham S.Salama, Reda A. El-Khoribi, Mahmoud E. Shoman
2014 International Journal of Computer Applications  
Then, the Hidden Markov Model (HMM) classifier is applied on the combined feature vector of acoustic and visual components.  ...  Multimodal speech recognition can be used to enhance the robustness of disordered speech.  ...  the same frame rate as audio features HMM Classification Hidden Markov Models (HMM) [20, 21] is proven to be a high reliable classifier for speech recognition applications for automatic speech recognition  ... 
doi:10.5120/16770-6337 fatcat:iz5egbg4jzcvffxy6yyr2tkpsu

Uncertainty Handling using Improvised Intuitionistic Fuzzy ANN based Voice Disorder Detection

2019 International journal of recent technology and engineering  
The simulation results proved the performance of the proposed model as best by producing more promising result while comparing with ANN, PANN and Fuzzy ANN models.  ...  This research work introduced an improvised intuitionistic fuzzy artificial neural network which enhances the process of voice disorder detection is SVD database by using analytical hierarchical processing  ...  Vahid Majidnezhad and Igor Kheidorov [5] developed a voice disorder detection model using Hidden Markov Model which categorizes speeches into two different classes namely normal voice and pathological  ... 
doi:10.35940/ijrte.a1424.078219 fatcat:wpxmqbylfbajxa4jg62cyhosnu

Media computing and applications for immersive communications: recent advances

Lei Xie, Janne Heikkilä, Bo Li
2017 Journal of Ambient Intelligence and Humanized Computing  
The proposed method achieves a higher accuracy of direction of arrival (DOA) estimation and source counting compared with other existing techniques.  ...  To avoid the redundancy and complexity of using microphone arrays, a soundfield microphone is adopted.  ...  In the ninth paper, titled "A Hybrid Neural Network Hidden Markov Model Approach for Automatic Story Segmentation", Yu et al. have proposed a hybrid neural network hidden Markov model (NN-HMM) approach  ... 
doi:10.1007/s12652-017-0559-4 fatcat:qv3l2ebsojd6pipzvbn3jjqtm4

Detection of Defective Speech Using Convolutional Neural Networks

Mikhail Belenko, Nikita Burym, Pavel Balakshin
2020 Majorov International Conference on Software Engineering and Computer Systems  
It is shown that the convolutional neural network effectively extracts features from the spectrograms of voice recordings and diagnoses voice disorders.  ...  The effect of the size of convolutional network filters on each layer on the system performance is also studied.  ...  However, with the increasing computing capabilities of hardware and the improvement of machine learning algorithms, the Markov model hidden in the deep neural network gradually replaces the traditional  ... 
dblp:conf/micsecs/BelenkoBB20 fatcat:gmpippgys5b6dmdmw6ztivelxy

Computer Aided Recognition of Vocal Folds Disorders by Means of RASTA-PLP

Ali Salih Mahmoud Saudi, Aliaa A. A. Youssif, Atef Z. Ghalwash
2012 Computer and Information Science  
In the context of the recognition of vocal folds disorders, the systems based on acoustic analysis are being introduced as computer aided medical diagnosis tools due to its objectivity and noninvasive  ...  Acoustic analysis is a complementary tool to those methods based on direct observation of the vocal folds by laryngoscopy; also, it can be used for the evaluation of surgical operation.  ...  Acknowledgment The authors would like to thank Dr. Ahmed Eldemerdash, Phoniatrics department, Kobri Elkobba Hospital for his constant encouragement and for the grants given to this work.  ... 
doi:10.5539/cis.v5n2p39 fatcat:zpeumktwtna75bmccb23jeg6za

Automatic Speech Segmentation Based On Audio and Optical Flow Visual Classification

Behnam Torabi, Ahmad Reza Naghsh Nilchi
2014 International Journal of Image Graphics and Signal Processing  
Then, Hidden Markov Models are trained to segment audio signals from image sequences and audio features based on extracted optical flow.  ...  Automatic speech segmentation as an important part of speech recognition system (ASR) is highly noise dependent.  ...  The average of these vectors is calculated in various ways and every single of them is used plus the audio data as the input for Hidden Markov Model.  ... 
doi:10.5815/ijigsp.2014.11.06 fatcat:rz4gn6nr3bhnvml44kwlqvz7pq

Automatic dysfluency detection in dysarthric speech using deep belief networks

Stacey Oue, Ricard Marxer, Frank Rudzicz
2015 Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies  
This paper investigates different types of input features used by deep neural networks (DNNs) to automatically detect repetition stuttering and non-speech dysfluencies within dysarthric speech.  ...  Dysarthria is a speech disorder caused by difficulties in controlling muscles, such as the tongue and lips, that are needed to produce speech.  ...  Acknowledgements This work is partially funded by Thotra Incorporated, of which Frank Rudzicz is the CEO.  ... 
doi:10.18653/v1/w15-5111 dblp:conf/slpat/OueMR15 fatcat:mkoz6lwpafeojbja66atux3r6q
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