Predicting fundamental frequency from mel-frequency cepstral coefficients to enable speech reconstruction

Shao, Xu and Milner, Ben P. (2005) Predicting fundamental frequency from mel-frequency cepstral coefficients to enable speech reconstruction. Journal of the Acoustical Society of America, 118 (2). pp. 1134-1143. ISSN 1520-8524

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Abstract

This work proposes a method to reconstruct an acoustic speech signal solely from a stream of mel-frequency cepstral coefficients (MFCCs) as may be encountered in a distributed speech recognition (DSR) system. Previous methods for speech reconstruction have required, in addition to the MFCC vectors, fundamental frequency and voicing components. In this work the voicing classification and fundamental frequency are predicted from the MFCC vectors themselves using two maximum a posteriori (MAP) methods. The first method enables fundamental frequency prediction by modeling the joint density of MFCCs and fundamental frequency using a single Gaussian mixture model (GMM). The second scheme uses a set of hidden Markov models (HMMs) to link together a set of state-dependent GMMs, which enables a more localized modeling of the joint density of MFCCs and fundamental frequency. Experimental results on speaker-independent male and female speech show that accurate voicing classification and fundamental frequency prediction is attained when compared to hand-corrected reference fundamental frequency measurements. The use of the predicted fundamental frequency and voicing for speech reconstruction is shown to give very similar speech quality to that obtained using the reference fundamental frequency and voicing.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI
Depositing User:	EPrints Services
Date Deposited:	01 Oct 2010 13:41
Last Modified:	10 Dec 2024 01:21
URI:	https://ueaeprints.uea.ac.uk/id/eprint/3052
DOI:	10.1121/1.1953269

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