A GMM-Based Handset Selector for Channel Mismatch Compensation with Applications to Speaker Identification

Authors:
K. K. Yiu;M. W. Mak;S. Y. Kung
Affiliations:
-;-;-
Venue:
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Year:
2001

Citing 3
Cited 1

Deconvolution of telephone line effects for speech recognition

Speech Communication
Handset-Dependent Background Models for Robust Text-Independent Speaker Recognition

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Estimation of elliptical basis function parameters by the EM algorithm with application to speaker verification

IEEE Transactions on Neural Networks

Sun-Yuan Kung, Speaker Verification from Coded Telephone Speech Using Stochastic Feature Transformation and Handset Identification

PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing

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Abstract

In telephone-based speaker identification, variation in handset characteristics can introduce severe speech variability even for speech uttered by the same speaker. This paper proposes a method, a number of Gaussian mixture models are independently trained to identify the most likely handset given a test utterance. The identified handset is used to select a compensation vector from a set of pre-computed vectors, where the pre-computed vectors are the average frame-by-frame differences between the clean and distorted utterance. The clean features are than recovered by subtracting the selected compensation vector from the distorted vectors. Experimental results based on 138 speakers of the YOHO and telephone YOHO corpora show that the proposed approach is computationally efficient and is able to increase the accuracy from 17% (without compensation) to 85% (with compensation).