NDLI: Improved class definition in two dimensional linear discriminant analysis of speech

Content Provider	IEEE Xplore Digital Library
Author	Conka, D. Viszlay, P. Juhar, J.
Copyright Year	2015
Description	Author affiliation: Dept. of Electron. & Multimedia Commun., Tech. Univ. of Kosice, Kosice, Slovakia (Conka, D.; Viszlay, P.; Juhar, J.)
Abstract	Two-dimensional linear discriminant analysis (2DLDA) is a popular feature transformation being applied in current automatic speech recognition (ASR). The parameters of 2DLDA are usually computed on labelled training data partitioned into phonetic classes. It is generally known that one phonetic class contains speech data collected from different speakers with different speech variability and context for the same phonetic unit. Therefore, many clusters exist in each phonetic class. The mentioned effects are not taken into account in the conventional 2DLDA. In this paper, we present an efficient improvement of 2DLDA, which involves the well-known K-means clustering technique to modify the standard class definition. The clustering algorithm is used to identify the existing clusters in the basic classes, which are treated as the new classes for the subsequent 2DLDA estimation. The proposed method is thoroughly evaluated in Slovak triphone-based large vocabulary continuous speech recognition (LVCSR) task. The modified 2DLDA is compared to the state-of-the-art Mel-frequency cepstral coefficients (MFCCs) and to conventional LDA. The results show that the modified 2DLDA features outperform the MFCCs, LDA and also lead to improvement over the conventional 2DLDA.
Starting Page	261
Ending Page	263
File Size	300742
Page Count	3
File Format	PDF
ISBN	9781479981175
e-ISBN	9781479981199
DOI	10.1109/RADIOELEK.2015.7129025
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-21
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	triphone-level class discriminant analysis Computational modeling Hidden Markov models Speech recognition Speech Acoustics Linear discriminant analysis cluster analysis scatter matrix Standards
Content Type	Text
Resource Type	Article

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