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Title: Perceptually-based features in automatic speech recognition
Author: Gu, Y.
Awarding Body: University College of Swansea
Current Institution: Swansea University
Date of Award: 1991
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Interspeaker variability of speech features is one of most important problems in automatic speech recognition (ASR), and makes speaker-independent systems much more difficult to achieve than speaker-dependent ones. The work described in the Thesis examines two ideas to overcome this problem. The first attempts to extract more reliable speech features by perceptually-based modelling; the second investigates the speaker variability in this speech feature and reduces its effects by a speaker normalisation scheme. The application of human speech perception in automatic speech recognition is discussed in the Thesis. Several perceptually-based feature analysis techniques are compared in terms of recognition performance, and the effects of individual perceptual parameter encompassed in the feature analysis are investigated. The work demonstrates the benefits of perceptual feature analysis (particularly perceptually-based linear predictive approach) compared with the conventional linear predictive analysis technique. The proposal for speaker normalisation is based on a regional-continuous linear matrix transform function on the perceptual feature space, with an automatic feature classification. This approach is applied in an ASR adaptation system. It is shown that the recognition error rate reduces rapidly when using a few words or a single sentence for adaptation. The adaptation performance demonstrates that such an approach could be very promising for a large vocabulary speaker-independent system.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available