Discriminative likelihood score weighting based on acoustic-phonetic classification for speaker identification

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In this paper, a new discriminative likelihood score weighting technique is proposed for speaker identification. The proposed method employs a discriminative weighting of frame-level log-likelihood scores with acoustic-phonetic classification in the Gaussian mixture model (GMM)-based speaker identification. Experiments performed on the Aurora noise-corrupted TIMIT database showed that the proposed approach provides meaningful performance improvement with an overall relative error reduction of 15.8% over the maximum likelihood-based baseline GMM approach.
Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
Issue Date
2014-08
Language
English
Article Type
Article
Keywords

SPEECH RECOGNITION; VERIFICATION; MODELS

Citation

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING

ISSN
1687-6180
DOI
10.1186/1687-6180-2014-126
URI
http://hdl.handle.net/10203/192599
Appears in Collection
EE-Journal Papers(저널논문)
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