11,119 research outputs found
Automatic age detection in normal and pathological voice
Systems that automatically detect voice pathologies are usually trained with recordings belonging to population of all ages.
However such an approach might be inadequate because of the acoustic variations in the voice caused by the natural aging process. In top of that, elder voices present some perturbations in quality similar to those related to voice disorders, which make the detection of pathologies more troublesome. With this in mind, the study of methodologies which automatically incorporate information about speakers’ age, aiming at a simplification in the detection of voice disorders is of interest. In this respect, the present paper introduces an age detector trained with normal and pathological voice, constituting a first step towards the study of age-dependent pathology detectors. The proposed system employs sustained vowels of the Saarbrucken database from which two age groups are examinated: adults and elders. Mel frequency cepstral coefficients for characterization, and Gaussian mixture models for classification are utilized. In addition, fusion of vowels at score level is considered to improve detection performance. Results suggest that age might be effectively recognized using normal and pathological voices when using sustained vowels as acoustical material, opening up possibilities for the design of automatic age-dependent voice pathology detection systems
Automatic Detection of Laryngeal Pathology on Sustained Vowels Using Short-Term Cepstral Parameters: Analysis of Performance and Theoretical Justification
The majority of speech signal analysis procedures for automatic detection of laryngeal pathologies mainly rely on parameters extracted from time domain processing. Moreover, calculation of these parameters often requires prior pitch period estimation; therefore, their validity heavily depends on the robustness of pitch detection. Within this paper, an alternative approach based on cepstral- domain processing is presented which has the advantage of not requiring pitch estimation, thus providing a gain in both simplicity and robustness. While the proposed scheme is similar to solutions based on Mel-frequency cepstral parameters, already present in literature, it has an easier physical interpretation while achieving similar performance standards
Estimation of Severity of Speech Disability through Speech Envelope
In this paper, envelope detection of speech is discussed to distinguish the
pathological cases of speech disabled children. The speech signal samples of
children of age between five to eight years are considered for the present
study. These speech signals are digitized and are used to determine the speech
envelope. The envelope is subjected to ratio mean analysis to estimate the
disability. This analysis is conducted on ten speech signal samples which are
related to both place of articulation and manner of articulation. Overall
speech disability of a pathological subject is estimated based on the results
of above analysis.Comment: 8 pages,4 Figures,Signal & Image Processing Journal AIRC
Use of Mel Frequency Cepstral Coefficients for Automatic Pathology Detection on Sustained Vowel Phonations: Mathematical and Statistical Justification
This paper presents a justification for the use of MFCC parameters in automatic pathology detection on speech. While such an application has produced good results up to now, only partial explanations to this good performance had been given before. The herein exposed explanation consists of an interpretation of the mathematical transformations involved in MFCC calculation and a statistical analysis that confirms the conclusions drawn from the theoretical reasoning
Glottal-Source Spectral Biometry for Voice Characterization
The biometric signature derived from the estimation of the power spectral density singularities of a speaker’s glottal source is described in the present work. This consists in the collection of peak-trough profiles found in the spectral density, as related to the biomechanics of the vocal folds. Samples of parameter estimations from a set of 100 normophonic (pathology-free) speakers are produced. Mapping the set of speaker’s samples to a manifold defined by Principal Component Analysis and clustering them by k-means in terms of the most relevant principal components shows the separation of speakers by gender. This means that the proposed signature conveys relevant speaker’s metainformation, which may be useful in security and forensic applications for which contextual side information is considered relevant
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech
The rapid population aging has stimulated the development of assistive
devices that provide personalized medical support to the needies suffering from
various etiologies. One prominent clinical application is a computer-assisted
speech training system which enables personalized speech therapy to patients
impaired by communicative disorders in the patient's home environment. Such a
system relies on the robust automatic speech recognition (ASR) technology to be
able to provide accurate articulation feedback. With the long-term aim of
developing off-the-shelf ASR systems that can be incorporated in clinical
context without prior speaker information, we compare the ASR performance of
speaker-independent bottleneck and articulatory features on dysarthric speech
used in conjunction with dedicated neural network-based acoustic models that
have been shown to be robust against spectrotemporal deviations. We report ASR
performance of these systems on two dysarthric speech datasets of different
characteristics to quantify the achieved performance gains. Despite the
remaining performance gap between the dysarthric and normal speech, significant
improvements have been reported on both datasets using speaker-independent ASR
architectures.Comment: to appear in Computer Speech & Language -
https://doi.org/10.1016/j.csl.2019.05.002 - arXiv admin note: substantial
text overlap with arXiv:1807.1094
Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms
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 machine learning algorithms. The results of detection and classification of voice disorders obtained with the sustained vowel and the running speech are also compared with the results of an English-language disorder database, the Massachusetts Eye and Ear Infirmary (MEEI) database
Assessment of severe apnoea through voice analysis, automatic speech, and speaker recognition techniques
The electronic version of this article is the complete one and can be found online at:
http://asp.eurasipjournals.com/content/2009/1/982531This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.The activities described in this paper were funded by the Spanish Ministry of Science and Technology as part of the TEC2006-13170-C02-02 Project
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