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Comparison of Cepstral Normalization Techniques in Whispered Speech RecognitionGROZDIC, D. , JOVICIC, S. , SUMARAC PAVLOVIC, D. , GALIC, J. , MARKOVIC, B.
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automatic speech recognition, cepstral analysis, hidden Markov models, speech analysis, whisper
speech(26), recognition(13), whispered(12), hansen(6), whisper(5), signal(5), processing(5), jovicic(5), grozdic(4), boril(4)
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About this article
Date of Publication: 2017-02-28
Volume 17, Issue 1, Year 2017, On page(s): 21 - 26
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.01004
Web of Science Accession Number: 000396335900004
SCOPUS ID: 85014204959
This article presents an analysis of different cepstral normalization techniques in automatic recognition of whispered and bimodal speech (speech+whisper). In these experiments, conventional GMM-HMM speech recognizer was used as speaker-dependant automatic speech recognition system with special Whi-Spe corpus containing utterance recordings in normally phonated speech and whisper. The following normalization techniques were tested and compared: CMN (Cepstral Mean Normalization), CVN (Cepstral Variance Normalization), MVN (Cepstral Mean and Variance Normalization), CGN (Cepstral Gain Normalization) and quantile-based dynamic normalization techniques such as QCN and QCN-RASTA. The experimental results show to what extent each of these cepstral normalization techniques can improve whisper recognition accuracy in mismatched train/test scenario. The best result is obtained using CMN in combination with inverse filtering which provides an average 39.9 percent improvement in whisper recognition accuracy for all tested speakers.
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