NDLI: Pathological speech deformation degree assessment based on integrating feature and neural network

Content Provider	IEEE Xplore Digital Library
Author	Wang Xu Han Zhiyan Wang Jian
Copyright Year	2008
Description	Author affiliation: Coll. of Inf. Sci.&Eng., Northeastern Univ., Shenyang (Wang Xu; Han Zhiyan; Wang Jian)
Abstract	In tasks related to the analysis and recognition of pathological speech it is often more important to provide the respective person (e.g. physician) with guidelines for a deformation degree assessment of speech signal than to achieve a very accurate automated recognition. By ear it is easy to judge whether the speech is regular or deformed, but any attempt of a deformation degree evaluation is not satisfactory. According to above status, we presented a deformation degree assessment system of speech signal based on integrating feature and neural network. The system is comprised of four main sections, a pre-processing section, a feature extracting section, a neural network processing section and assessment value calculation section. And also this paper integrates different speech features to calculate the perceptual distance vector to improve assessment ratio, the perceptual distance between the pathological speech and the normal speech under test is used as input to the neural network. The simulation results demonstrate that a classification accuracy of 95% is obtained with database of 80 speech signals (40 normal and 40 pathological cases).
Starting Page	441
Ending Page	444
File Size	100934
Page Count	4
File Format	PDF
ISBN	9787900719706
DOI	10.1109/CHICC.2008.4605027
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute Of Intelligence M&C Technical Of KMUST
Subject Keyword	Wavelet transforms Pathology Neural Network Speech Deformation Degree Artificial neural networks Transforms Speech Feature extraction Time frequency analysis Integrating Feature
Content Type	Text
Resource Type	Article

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