Formant analysis in dysphonic patients and automatic Arabic digit speech recognition (original) (raw)
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An Automatic Diagnostic System for Medically Disordered Voice
The automatic detection of vocal cord/vocal tract diseases from the medically disordered speech is a challenging task in the medical field. An automatic diagnostic system for the medically disordered voice is proposed in this paper. The developed system takes speech recorded by a patient having a disorder in his/her voice, as an input. The system gives the type of voice disorder as an output. The output is accompanied by a confidence score that tells about the systems confidence of correctly recognizing the type of the disorder as output. In the system, after the detection of the vowels from the disordered speech, two formants F1 and F2 are extracted from the vowels and Euclidean distance is used for classification. The results of the proposed system are encouraging.
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2016
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Journal of Voice, 2020
The goal of this study is to compare and combine different acoustic features in discriminating subjects with and without voice disorders. A database of 484 adult patients participated in the research. All subjects recorded a sustained vowel /Ɛ/ and underwent a laryngoscopic examination of the larynx. From the results of the laryngeal examination performed by a physician and the auditory-perceptual judgment performed by a Speech-Language Pathologist, the subjects were allocated to the group with (n = 52) and without (n = 432) voice disorder. Four types of acoustic features were used: traditional measures, cepstral measures, nonlinear measures, and recurrence quantification measures. Recordings comprised the emission of the vowel /e/. Quadratic discriminant analysis was used as classifier. Individual features in the context of traditional, cepstral, and recurrence quantification measures achieved an acceptable performance of ≥70%. Combination of measures improved the classifier performance. The best classification result (86.43% accuracy) was obtained by combining traditional linear and recurrence quantification measures. Results shown that Traditional, Cepstral, and recurrence quantification measures are promising features that capture meaningful information about voice production, which provides good classification performances. The findings of this study can be used to develop a computational tool for voice disorders diagnosis and monitoring.
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2005
This paper investigates the adaptation of Automatic Speaker Recognition (ASR) techniques to the pathological voice assessment (dysphonic voices). The aim of this study is to provide a novel method, suitable for keeping track of the evolution of the patient's pathology: easy-to-use, fast, non-invasive for the patient, and affordable for the clinicians. This method will be complementary to the existing ones -the perceptual judgment and the usual objective measurement (jitter, airflows...) which remain time and human resource consuming. The system designed for this particular task relies on the GMMbased approach, which is the state-of-the-art for speaker recognition. It is derived from the open source ASR tools (LIA_Spk-Det and ALIZE) of the LIA lab. Experiments conducted on a dysphonic corpus provide promising results, underlining the interest of such an approach and opening further research investigation.
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International Journal of Advanced Computer Science and Applications
This paper addresses the realization of a Human/Machine (H/M) interface including a system for automatic recognition of the Continuous Pathological Speech (ARSCPS) and several communication tools in order to help frail people with speech problems (Dysarthric speech) to access services providing by new technologies of information and communication (TIC) while making it easier for the doctors to achieve a first diagnosis on the patient's disease. In addition, an ARSCPS has been improved and developed for normal and pathology voice while establishing a link with our graphic interface which is based on the box tools Hidden Markov Model Toolkit (HTK), in addition to the Hidden Models of Markov (HMM). In our work we used different techniques of feature extraction for the speech recognition system in order to improve the dysarthric speech intelligibility while developing an ARSCPS which can perform well for pathological and normal speakers. These techniques are based on the coefficients of ETSI standard Mel Frequency Cepstral Coefficient Front End (ETSI MFCC FE V2.0); Perceptual Linear Prediction coefficients (PLP); Mel Frequency Cepstral Coefficients (MFCC) and the recently proposed Power Normalized Cepstral Coefficients (PNCC) have been used as a basis for comparison. In this context we used the Nemours database which contains 11 speakers that represents dysarthric speech and 11 speakers that represents normal speech.
Journal of healthcare engineering, 2017
A voice disorder database is an essential element in doing research on automatic voice disorder detection and classification. Ethnicity affects the voice characteristics of a person, and so it is necessary to develop a database by collecting the voice samples of the targeted ethnic group. This will enhance the chances of arriving at a global solution for the accurate and reliable diagnosis of voice disorders by understanding the characteristics of a local group. Motivated by such idea, an Arabic voice pathology database (AVPD) is designed and developed in this study by recording three vowels, running speech, and isolated words. For each recorded samples, the perceptual severity is also provided which is a unique aspect of the AVPD. During the development of the AVPD, the shortcomings of different voice disorder databases were identified so that they could be avoided in the AVPD. In addition, the AVPD is evaluated by using six different types of speech features and four types of mach...
IJERT-Diagnosis of Disordered Speech using Automatic Speech Recognition
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/diagnosis-of-disordered-speech-using-automatic-speech-recognition https://www.ijert.org/research/diagnosis-of-disordered-speech-using-automatic-speech-recognition-IJERTCONV8IS11029.pdf The purpose of the project is to help the speech disordered people communicate effectively. Speech is the effective way of communication. Many speech disorders are caused due to stroke. In this paper speech disorders in adults and various software available in the market has been described. The people suffering from speech disorders produce a 17.6% of mispronunciations in the phonetic level. The study of speech signals and the methods to process them is called speech processing. In order to study the speech signals methods such as speech coding, speech synthesis, speech recognition and speaker recognition plays a vital role. Among all these, speech recognition is considered in this paper. Speech recognition is converting the acoustic signals obtained from the speaker which is given as an input to microphone. The obtained input is then generated as a set of words. There must be electronic circuits in order to extract linguistic properties obtained from the speaker. This project is designed using MFCC as a feature extractor and support vector machine as the classifier.
Logopedics Phonatrics …, 2009
In general opinion computerized automatic speech recognition (ASR) seems to be regarded as a method only to accomplish transcriptions from spoken language to written text and as such quite insecure and rather cumbersome. However, due to great advances in computer technology and informatics methodology ASR has nowadays become quite dependable and easier to handle, and the number of applications has increased considerably. After some introductory background information on ASR a number of applications of great interest for professionals in voice, speech, and language therapy are pointed out. In the foreseeable future, the keyboard and mouse will by means of ASR technology be replaced in many functions by a microphone as the humanÁcomputer interface, and the computer will talk back via its loud-speaker. It seems important that professionals engaged in the care of oral communication disorders take part in this development so their clients may get the optimal benefit from this new technology.