Automatic Recognition of Pathological Phoneme Production

Wielgat, Robert; Zieliński, Tomasz P.; Woźniak, Tomasz; Grabias, Stanisław; Król, Daniel

doi:10.1159/000170083

Cited by 12 publications

(6 citation statements)

References 5 publications

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“…Automated systems have been used to detect the presence of pathological voice, specifically vocal fold (i.e., laryngeal) disorders (Arias-Londoñ o et al, 2010;Fraile et al, 2009;Szaleniec et al, 2007;Wielgat et al, 2008), and assess the intelligibility of subjects with dysglossia and dysphonia to assist in rehabilitation (Maier et al, 2010). In these disorders, the speech production organs are affected, which results in atypicalities in the voice.…”

Section: Speech Technology Tools In Disordered Voice and Speech Therapymentioning

confidence: 99%

Tabby Talks: An automated tool for the assessment of childhood apraxia of speech

Shahin

Ahmed

Parnandi

et al. 2015

Speech Communication

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Section: Speech Technology Tools In Disordered Voice and Speech Therapymentioning

confidence: 99%

Tabby Talks: An automated tool for the assessment of childhood apraxia of speech

Shahin

Ahmed

Parnandi

et al. 2015

Speech Communication

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“…Their evaluation revealed that the root mean squared error of the discrepancies between perceived and computed intelligibilities can be as low as 8 on a scale of 0 to 100. Automatic recognition of Polish words was carried out in the study Wielgat et al [ 10 ], where the input was speech from voice disordered Polish children. They used MFCC and human factor cepstral coefficients (HFCC) to recognize words with confusing phonemes.…”

Section: Introductionmentioning

confidence: 99%

Formant analysis in dysphonic patients and automatic Arabic digit speech recognition

et al. 2011

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Background and objectiveThere has been a growing interest in objective assessment of speech in dysphonic patients for the classification of the type and severity of voice pathologies using automatic speech recognition (ASR). The aim of this work was to study the accuracy of the conventional ASR system (with Mel frequency cepstral coefficients (MFCCs) based front end and hidden Markov model (HMM) based back end) in recognizing the speech characteristics of people with pathological voice.Materials and methodsThe speech samples of 62 dysphonic patients with six different types of voice disorders and 50 normal subjects were analyzed. The Arabic spoken digits were taken as an input. The distribution of the first four formants of the vowel /a/ was extracted to examine deviation of the formants from normal.ResultsThere was 100% recognition accuracy obtained for Arabic digits spoken by normal speakers. However, there was a significant loss of accuracy in the classifications while spoken by voice disordered subjects. Moreover, no significant improvement in ASR performance was achieved after assessing a subset of the individuals with disordered voices who underwent treatment.ConclusionThe results of this study revealed that the current ASR technique is not a reliable tool in recognizing the speech of dysphonic patients.

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“…Also, a different approach to objectively measure the speech quality could be an analysis of speech features present in speech such as nasality or voicing. Such a complex task of calculating speech features could be performed via automatic speech recognition using a neural network trained in identification of speech features [24][25][26] . This approach might give additional insight into the speech of patients treated for head and neck cancer.…”

Section: Discussionmentioning

confidence: 99%

Objective Acoustic-Phonetic Speech Analysis in Patients Treated for Oral or Oropharyngeal Cancer

Bruijn

Bosch

Kuik

et al. 2009

Folia Phoniatr Logop

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Objective: Speech impairment often occurs in patients after treatment for head and neck cancer. New treatment modalities such as surgical reconstruction or (chemo)radiation techniques aim at sparing anatomical structures that are correlated with speech and swallowing. In randomized trials investigating efficacy of various treatment modalities or speech rehabilitation, objective speech analysis techniques may add to improve speech outcome assessment. The goal of the present study is to investigate the role of objective acoustic-phonetic analyses in a multidimensional speech assessment protocol. Patients and Methods: Speech recordings of 51 patients (6 months after reconstructive surgery and postoperative radiotherapy for oral or oropharyngeal cancer) and of 18 control speakers were subjectively evaluated regarding intelligibility, nasal resonance, articulation, and patient-reported speech outcome (speech subscale of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Head and Neck 35 module). Acoustic-phonetic analyses were performed to calculate formant values of the vowels /a, i, u/, vowel space, air pressure release of /k/ and spectral slope of /x/. Results: Intelligibility, articulation, and nasal resonance were best predicted by vowel space and /k/. Within patients, /k/ and /x/ differentiated tumor site and stage. Various objective speech parameters were related to speech problems as reported by patients. Conclusion: Objective acoustic-phonetic analysis of speech of patients is feasible and contributes to further development of a speech assessment protocol.

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Automatic Recognition of Pathological Phoneme Production

Cited by 12 publications

References 5 publications

Tabby Talks: An automated tool for the assessment of childhood apraxia of speech

Tabby Talks: An automated tool for the assessment of childhood apraxia of speech

Formant analysis in dysphonic patients and automatic Arabic digit speech recognition

Objective Acoustic-Phonetic Speech Analysis in Patients Treated for Oral or Oropharyngeal Cancer

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