Measure and Comparison of Speech Pause Duration in Subjects with Disfluency Speech

Teixeira, João Paulo; Fernandes, Maria G.; Costa, Rita Alexandra

doi:10.1016/j.protcy.2012.09.090

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…In this group the percentage of silence varies from 20.0 to 41.8%. Subject number 2, presenting 41.8% of silence is the subject among the 4 disfluent belonging to the sample under study that will present a disfluency more accentuated as recorded in Table 1, on the previous study (Teixeira, Fernandes & Costa, 2012;Teixeira, Fernandes & Costa, 2013). Based on results presented in Tables 1 and 2 it can be alleged that it is not a viable classification of degree of disfluency solely based on the percentages of silence.…”

Section: % Of Silence Time Of Silencementioning

confidence: 97%

“…Riley's Stuttering Severity Instrument is one of the most common scales (Riley, 1972). Through this tool, a previous study was developed to classify the degree of severity of stuttering in a group of six subjects (Teixeira, Fernandes & Costa, 2012;Teixeira, Fernandes & Costa, 2013). The typology followed was similar to that of author Wendell Johnson (1963), since it showed episodes of stutter that fit more with the scale of measurement of the degree of severity of disfluency.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Determination of Pauses in Speech for Classification of Stuttering Disorder

Teixeira

Fernandes

Costa

2017

Advances in Healthcare Information Systems and Administration

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An algorithm to automatically identify segments of silence or speech is presented. The algorithm was developed to measure the silence periods in spontaneous and read speech. These silence periods are one of the parameters used to know the degree of severity of stuttered speech. For this purpose the three longer disfluent events (pauses or other disfluent events) and also the percentage of silence are useful. The algorithm is based on the evaluation of the energy and the zero crossing rate of the signal compared to the threshold values previously determined in silence. One experiment with eight subjects is described using the Stuttering Severity Instrument for Children and Adults – SSI and the percentage of silence in speech. It was concluded that the percentage of silence is good enough to separate stuttered from the normal speech but alone is not capable of measuring the degree of severity of the stuttered speech.

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Section: % Of Silence Time Of Silencementioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Automatic Determination of Pauses in Speech for Classification of Stuttering Disorder

Teixeira

Fernandes

Costa

2017

Advances in Healthcare Information Systems and Administration

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Neural Network based Speech Assistance tool to enhance the fluency of adults who stutter

Narasimhan

Rao

2019

2019 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

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Détails phonétiques dans la réalisation des pauses en Français : étude de parole lue en langue maternelle vs en langue étrangère

Fauth¹,

Trouvain²

2018

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Dans ce travail, nous étudierons la réalisation de différents types de pauses (silencieuses, respiratoires et remplies) en fonction de la compétence linguistique en français langue étrangère. 30 locuteurs (10 natifs, 10 débutants, 10 avancés) ont été enregistrés en lecture. Comme attendu, les apprenants produisent plus de pauses et les pauses respiratoires sont les plus fréquentes pour l’ensemble des locuteurs. Étonnamment, les natifs produisent des pauses vides plus longues, probablement parce qu’elles sont principalement respiratoires. Dans nos données, les pauses silencieuses sont rares et sont généralement produites par les apprenants dans les phases disfluentes, alors que les pauses avec respiration audible (et donc non silencieuses) sont fréquentes et généralement réalisées aux frontières syntaxiques.

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Measure and Comparison of Speech Pause Duration in Subjects with Disfluency Speech

Cited by 3 publications

References 2 publications

Automatic Determination of Pauses in Speech for Classification of Stuttering Disorder

Automatic Determination of Pauses in Speech for Classification of Stuttering Disorder

Neural Network based Speech Assistance tool to enhance the fluency of adults who stutter

Détails phonétiques dans la réalisation des pauses en Français : étude de parole lue en langue maternelle vs en langue étrangère

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