Detail publikace

Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation

Originální název

Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation

Anglický název

Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation

Jazyk

en

Originální abstrakt

Up to 90% of patients with Parkinson’s disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dysfluency and speech quality deterioration extracted from a specialized poem recitation task to discriminate dysarthric and healthy speech. For this purpose, 152 speakers (53 healthy speakers, 99 PD patients) were examined. Only mildly strong correlation between speech features and clinical status of the speakers was observed. In the case of univariate classification analysis, sensitivity of 62.63% (imprecise articulation), 61.62% (dysprosody), 71.72% (speech dysfluency) and 59.60% (speech quality deterioration) was achieved. Multivariate classification analysis improved the classification performance. Sensitivity of 83.42% using only two features describing imprecise articulation and speech quality deterioration in HD was achieved. We showed the promising potential of the selected speech features and especially the use of poem recitation task to quantify and identify HD in PD.

Anglický abstrakt

Up to 90% of patients with Parkinson’s disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dysfluency and speech quality deterioration extracted from a specialized poem recitation task to discriminate dysarthric and healthy speech. For this purpose, 152 speakers (53 healthy speakers, 99 PD patients) were examined. Only mildly strong correlation between speech features and clinical status of the speakers was observed. In the case of univariate classification analysis, sensitivity of 62.63% (imprecise articulation), 61.62% (dysprosody), 71.72% (speech dysfluency) and 59.60% (speech quality deterioration) was achieved. Multivariate classification analysis improved the classification performance. Sensitivity of 83.42% using only two features describing imprecise articulation and speech quality deterioration in HD was achieved. We showed the promising potential of the selected speech features and especially the use of poem recitation task to quantify and identify HD in PD.

BibTex


@inproceedings{BUT135600,
  author="Ján {Mucha} and Zoltán {Galáž} and Jiří {Mekyska} and Tomáš {Kiska} and Vojtěch {Zvončák} and Zdeněk {Smékal} and Ilona {Eliášová} and Martina {Mračková} and Milena {Košťálová} and Irena {Rektorová}",
  title="Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation",
  annote="Up to 90% of patients with Parkinson’s disease
(PD) suffer from hypokinetic dysarthria (HD). In this work,
we analysed the power of conventional speech features quantifying
imprecise articulation, dysprosody, speech dysfluency and
speech quality deterioration extracted from a specialized poem
recitation task to discriminate dysarthric and healthy speech.
For this purpose, 152 speakers (53 healthy speakers, 99 PD
patients) were examined. Only mildly strong correlation between
speech features and clinical status of the speakers was observed.
In the case of univariate classification analysis, sensitivity of
62.63% (imprecise articulation), 61.62% (dysprosody), 71.72%
(speech dysfluency) and 59.60% (speech quality deterioration)
was achieved. Multivariate classification analysis improved the
classification performance. Sensitivity of 83.42% using only two
features describing imprecise articulation and speech quality
deterioration in HD was achieved. We showed the promising
potential of the selected speech features and especially the use of
poem recitation task to quantify and identify HD in PD.",
  booktitle="40th Anniversary of International Conference on Telecommunications and Signal Processing (TSP)",
  chapter="135600",
  doi="10.1109/TSP.2017.8076086",
  howpublished="online",
  year="2017",
  month="july",
  pages="735--738",
  type="conference paper"
}