Abstract
AbstractSpeaker normalization techniques for correcting differences in the vocal tract lengths of different speakers, referred to as vocal tract length normalization, in a large vocabulary voice recognition system using a hidden Markov model (HMM), have been proposed in recent years. In this paper, a scheme for approximating especially small changes in the vocal tract length by linear mapping using a vocal tract length parameter in cepstrum space and maximum‐likelihood estimation of this parameter from vocalization is proposed. The proposed method can estimate a more optimal parameter for a speaker with a small amount of computation than in past schemes using multiple vocal tract length parameters in advance. In evaluation tests of the recognition of 5000 single Japanese words, the proposed scheme decreased errors by 7.1% alone and 14.6% in combination with cepstrum mean normalization (CMN). © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(5): 30–40, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.1125
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
