NDLI: Approximately independent factors of speech using nonlinear symplectic transformation

Content Provider	IEEE Xplore Digital Library
Author	Omar, M.K. Hasegawa-Johnson, M.
Copyright Year	1993
Abstract	This paper addresses the problem of representing the speech signal using a set of features that are approximately statistically independent. This statistical independence simplifies building probabilistic models based on these features that can be used in applications like speech recognition. Since there is no evidence that the speech signal is a linear combination of separate factors or sources, we use a more general nonlinear transformation of the speech signal to achieve our approximately statistically independent feature set. We choose the transformation to be symplectic to maximize the likelihood of the generated feature set. In this paper, we describe applying this nonlinear transformation to the speech time-domain data directly and to the Mel-frequency cepstrum coefficients (MFCC). We discuss also experiments in which the generated feature set is transformed into a more compact set using a maximum mutual information linear transformation. This linear transformation is used to generate the acoustic features that represent the distinctions among the phonemes. The features resulted from this transformation are used in phoneme recognition experiments. The best results achieved show about 2% improvement in recognition accuracy compared to results based on MFCC features.
Starting Page	660
Ending Page	671
Page Count	12
File Size	487919
File Format	PDF
ISSN	10636676
Volume Number	11
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Independent component analysis Speech analysis Automatic speech recognition Character recognition Mel frequency cepstral coefficient Information theory Time domain analysis Cepstrum Mutual information
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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