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[ subject:"Computer engineering." ]
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Towards an optimized feature set to ...
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Nagaraja, Sunil.
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Towards an optimized feature set to assess acoustic perturbations in dysarthric speech.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Towards an optimized feature set to assess acoustic perturbations in dysarthric speech./
作者:
Nagaraja, Sunil.
面頁冊數:
76 p.
附註:
Source: Masters Abstracts International, Volume: 51-03.
Contained By:
Masters Abstracts International51-03(E).
標題:
Computer engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR89081
ISBN:
9780494890813
Towards an optimized feature set to assess acoustic perturbations in dysarthric speech.
Nagaraja, Sunil.
Towards an optimized feature set to assess acoustic perturbations in dysarthric speech.
- 76 p.
Source: Masters Abstracts International, Volume: 51-03.
Thesis (M.Sc.E.)--University of New Brunswick (Canada), 2011.
This research concerns the optimization of acoustic features of a biomedical speech processing tool developed to perform acoustic studies of dysarthric speech. The solution analyses the objective acoustic features derived to characterize the acoustic perturbations encountered in a group of neurological disorders known as Dysarthria. Dysarthria is a name given to a group of neurological disorders among which are: flaccid dysarthria, spastic dysarthria, ataxic dysarthria, Parkinson's disease, chorea, dystonia, organic voice tremor and amyotrophic lateral sclerosis. This work is concentrated on optimizing the low-level features derived to characterize the acoustic perturbations encountered in the disordered speech.
ISBN: 9780494890813Subjects--Topical Terms:
621879
Computer engineering.
Towards an optimized feature set to assess acoustic perturbations in dysarthric speech.
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76 p.
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Source: Masters Abstracts International, Volume: 51-03.
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Adviser: Eduardo Castillo Guerra.
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Thesis (M.Sc.E.)--University of New Brunswick (Canada), 2011.
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This research concerns the optimization of acoustic features of a biomedical speech processing tool developed to perform acoustic studies of dysarthric speech. The solution analyses the objective acoustic features derived to characterize the acoustic perturbations encountered in a group of neurological disorders known as Dysarthria. Dysarthria is a name given to a group of neurological disorders among which are: flaccid dysarthria, spastic dysarthria, ataxic dysarthria, Parkinson's disease, chorea, dystonia, organic voice tremor and amyotrophic lateral sclerosis. This work is concentrated on optimizing the low-level features derived to characterize the acoustic perturbations encountered in the disordered speech.
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The effectiveness of the features to provide relevant information of the disorders is being evaluated with various classification techniques: Linear Discriminant Analysis (LDA), Uncorrelated Linear Discriminant Analysis (ULDA), Support Vector Machine (SVM), Self-Organizing Map Networks (SOMN), and Learning Vector Quantization (LVQ). ULDA provides the highest classification index (90.6%) followed by LDA (85.0%) and SVM (83.4%).
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