Automatic detection of voice impairments from text-dependent running speech

Godino-Llorente, Juan Ignacio; Fraile, Rubén; Sáenz-Lechón, Nicolás; Osma-Ruiz, Víctor; Gómez-Vilda, Pedro

doi:10.1016/j.bspc.2009.01.007

Cited by 44 publications

(19 citation statements)

References 24 publications

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“…Time derivatives of MFCCs can capture only coarticulation of the phonemes. Conversely, as the proposed MDR method involves regression along four directions, it can encode voice onset, offset, fall and rise of consonants, and slowly varying nature of vowels in different frequencies, and thereby provide more distinctive information than the method of Godino-Llorente et al's study 30 does.…”

Section: E25mentioning

confidence: 96%

“…In Godino-Llorente et al's study, 30 it was shown that a system with MFCC features and multilayer perceptron classifier obtained 96.3% accuracy in voice disorder detection using running speech. The authors used a voiced/unvoiced detector to discard features from unvoiced segments.…”

Section: E25mentioning

confidence: 98%

“…The proposed MDR method, on the other hand, could achieve 97.8% accuracy in text independent case without adding the complexity of the voiced/unvoiced detector. In GodinoLlorente et al's study, 30 the authors used MFCCs, their time derivatives, and three noise measurements. Time derivatives of MFCCs can capture only coarticulation of the phonemes.…”

Section: E25mentioning

confidence: 99%

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Multidirectional Regression (MDR)-Based Features for Automatic Voice Disorder Detection

et al. 2012

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Section: E25mentioning

confidence: 96%

Section: E25mentioning

confidence: 98%

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Multidirectional Regression (MDR)-Based Features for Automatic Voice Disorder Detection

et al. 2012

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“…(16,17). Autorzy korzystali z bazy komercyjnych próbek ludzkich głosów patologicznych i prawidło-Tabela 2.…”

Section: Wynikiunclassified

Nonlinear Acoustic Analysis in the Evaluation of Occupational Voice Disorders

Niebudek-Bogusz¹,

Grygiel²,

Strumiłło³

et al. 2013

Med Pr

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StreszczenieWprowadzenie: W ostatnich latach podkreśla się, że tworzenie głosu podlega również procesom nieliniowym, powodującym aperiodyczne drgania fałdów głosowych. Drgania te nie zawsze można scharakteryzować za pomocą konwencjonalnych parametrów akustycznych, takich jak wskaźniki perturbacji częstotliwości i amplitudy. Z tego powodu wzrasta zainteresowanie nieliniowymi metodami analizy akustycznej. Celem pracy była ocena możliwości zastosowania nieliniowej analizy cepstralnej z wyznaczaniem współczynników cepstralnych (mel frequency cepstral coefficients -MFCC) w diagnozowaniu zawodowych zaburzeń głosu. Materiał i metody: Badaniami objęto 275 próbek głosów patologicznych (głoski "a" w przedłużonej fonacji oraz 4 standaryzowanych zdań) rejestrowanych u nauczycielek z zawodowo uwarunkowanymi łagodnymi zmianami przerostowymi fałdów głosowych (tj. guzkami głosowymi, polipami) oraz 200 próbek głosów prawidłowych kobiet z grupy kontrolnej. Średnia wieku grupy badanej wynosiła 45 lat, a grupy kontrolnej -43 lata. W badaniach wykonano analizę próbek głosowych za pomocą nieliniowej analizy akustycznej, w której zastosowano parametry MFCC. Wyniki: Po klasyfikacji MFCC przy pomocy odwzorowania Sammona oraz tzw. Maszyny Wektorów Nośnych uzyskano dużą trafność badanej metody. W testach wykonanych dla 475 zarejestrowanych próbek głosu zaburzenia głosu zostały wykryte z 91-procentową czułością i 83-procentową specyficznością dla głoski "a", a dla badanych zdań z czułością i specyficznością w granicach 87-100%. Wnioski: Nieliniowa analiza akustyczna z zastosowaniem współczynników MFCC może być przydatnym, obiektywnym narzędziem potwierdzającym zmiany patologiczne głośni o podłożu zawodowym. Prowadzone są dalsze badania w tym kierunku. Med. Pr. 2013;64(1):29-35 Słowa kluczowe: nieliniowa analiza akustyczna, zawodowe zaburzenia głosu, łagodne zmiany przerostowe fałdów głosowych, ŁZFG Abstract Background: Over recent years numerous papers have stressed that production of voice is subjected to the nonlinear processes, which cause aperiodic vibrations of vocal folds. These vibrations cannot always be characterized by means of conventional acoustic parameters, such as measurements of frequency and amplitude perturbations. Thus, special attention has recently been paid to nonlinear acoustic methods. The aim of this study was to assess the applicability of nonlinear cepstral analysis, including the evaluation of mel cepstral coefficients (MFCC), in diagnosing occupational voice disorders. Material and methods: The study involved 275 voice samples of pathologic voice (sustained vowel "a" and four standardized sentences) registered in female teachers with the occupation-related benign vocal fold masses (BVFM), such as vocal nodules, polyps, and 200 voice samples of normal voices from the control group of females. The mean age of patients and controls was similar (45 vs. 43 years). Voice samples from both groups were analyzed, including MFCC evaluation. Results: MFCC classification using the Sammon Mapping and Support Vector Machines...

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“…On the other hand, voice signals are obtained non-invasively and computer-based analysis of voice data is used increasingly in monitoring of treatment outcomes and screening for laryngeal disorders [4][5][6][7][8][9][10][11]. Several measures computed from voice data are already widely used to quantify dysphonia changes and characterise outcomes of therapeutic and surgical treatment of laryngeal diseases [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data

et al. 2017

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Abstract:The aim of this study is a transparent tool for analysis of voice (sustained phonation /a/) and query data capable of providing support in screening for laryngeal disorders. In this work, screening is concerned with identification of potentially pathological cases by classifying subject's data into 'healthy' and 'pathological' classes as well as visual exploration of data and automatic decisions. A set of association rules and a decision tree, techniques lending themselves for exploration, were generated for pathology detection. Data pairwise similarities, estimated in a novel way, were mapped onto a 2D metric space for visual inspection and analysis. Accurate identification of pathological cases was observed on unseen subjects using the most discriminative query parameter and six audio parameters routinely used by otolaryngologists in a clinical practice: equal error rate (EER) of 11.1% was achieved using association rules and 10.2% using the decision tree. The EER was further reduced to 9.5% by combining results from these two classifiers. The developed solution can be a useful tool for Otolaryngology departments in diagnostics, education and exploratory tasks.

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Automatic detection of voice impairments from text-dependent running speech

Cited by 44 publications

References 24 publications

Multidirectional Regression (MDR)-Based Features for Automatic Voice Disorder Detection

Multidirectional Regression (MDR)-Based Features for Automatic Voice Disorder Detection

Nonlinear Acoustic Analysis in the Evaluation of Occupational Voice Disorders

A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data

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