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| rdfs:seeAlso
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| Description
| - The accurate changepoint detection of different signal segments is a frequent challenge in various worldwide application domains. With regard to speech utterances, the changepoints are related to significant spectral changes, mostly represented by the borders between two phonemes. The main aim of this study is to design a novel Bayesian autoregressive changepoint detector (BACD) and test its feasibility in the evaluation of fluency and articulatory disorders. The originality of the proposed method consists in its normalising of a posteriori probability using Bayesian evidence and the designing of a recursive algorithm for reliable practice. For further evaluation of the BACD, we used data from (a) 118 people with various severity of stuttering to assess the extent of speech disfluency using a short reading passage, and (b) 24 patients with early Parkinson’s disease and 22 healthy speakers for evaluation of articulation accuracy using fast syllable repetition. Subsequently, we designed two measures for each type of disorder. While speech disfluency has been related to greater distances between spectral changes, dysarthric inaccurate articulation has instead been associated with lower spectral changes. These findings have been confirmed by statistically significant differences which were achieved in separating several degrees of disfluency and distinguishing healthy from parkinsonian speakers. In addition, a significant correlation between the automatic assessment of speech fluency and the judgment of human experts was obtained. In conclusion, our designed method provides a cost-effective, easy applicable and freely available evaluation of the speech disorders as well as other areas requiring reliable techniques for changepoint detection. In a more modest scope, BACD may be used in diagnosis of disease severity, monitoring treatment, and support for therapists’ evaluation.
- The accurate changepoint detection of different signal segments is a frequent challenge in various worldwide application domains. With regard to speech utterances, the changepoints are related to significant spectral changes, mostly represented by the borders between two phonemes. The main aim of this study is to design a novel Bayesian autoregressive changepoint detector (BACD) and test its feasibility in the evaluation of fluency and articulatory disorders. The originality of the proposed method consists in its normalising of a posteriori probability using Bayesian evidence and the designing of a recursive algorithm for reliable practice. For further evaluation of the BACD, we used data from (a) 118 people with various severity of stuttering to assess the extent of speech disfluency using a short reading passage, and (b) 24 patients with early Parkinson’s disease and 22 healthy speakers for evaluation of articulation accuracy using fast syllable repetition. Subsequently, we designed two measures for each type of disorder. While speech disfluency has been related to greater distances between spectral changes, dysarthric inaccurate articulation has instead been associated with lower spectral changes. These findings have been confirmed by statistically significant differences which were achieved in separating several degrees of disfluency and distinguishing healthy from parkinsonian speakers. In addition, a significant correlation between the automatic assessment of speech fluency and the judgment of human experts was obtained. In conclusion, our designed method provides a cost-effective, easy applicable and freely available evaluation of the speech disorders as well as other areas requiring reliable techniques for changepoint detection. In a more modest scope, BACD may be used in diagnosis of disease severity, monitoring treatment, and support for therapists’ evaluation. (en)
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| Title
| - Bayesian changepoint detection for the automatic assessment of fluency and articulatory disorders
- Bayesian changepoint detection for the automatic assessment of fluency and articulatory disorders (en)
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| skos:prefLabel
| - Bayesian changepoint detection for the automatic assessment of fluency and articulatory disorders
- Bayesian changepoint detection for the automatic assessment of fluency and articulatory disorders (en)
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| skos:notation
| - RIV/68407700:21230/13:00201945!RIV14-MSM-21230___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
| - P(GAP102/12/2230), P(NT11460), P(NT12288), S, Z(MSM0021620849)
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| http://linked.open...iv/cisloPeriodika
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/13:00201945
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Changepoint detection; speech pathology; speech signal processing; disfluency, articulation disorder (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...odStatuVydavatele
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/nazevZdroje
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...vavai/riv/projekt
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| http://linked.open...UplatneniVysledku
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| http://linked.open...v/svazekPeriodika
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| http://linked.open...iv/tvurceVysledku
| - Rusz, Jan
- Vokřál, J.
- Čmejla, Roman
- Bergl, Petr
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| http://linked.open...ain/vavai/riv/wos
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| http://linked.open...n/vavai/riv/zamer
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| issn
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| number of pages
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| http://bibframe.org/vocab/doi
| - 10.1016/j.specom.2012.08.003
|
| http://localhost/t...ganizacniJednotka
| |
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