An Empirical Evaluation of Zero Resource Acoustic Unit Discovery

Acoustic unit discovery (AUD) is a process of automatically identifying a categorical acoustic unit inventory from speech and producing corresponding acoustic unit tokenizations. AUD provides an important avenue for unsupervised acoustic model training in a zero resource setting where expert-provided linguistic knowledge and transcribed speech are unavailable. Therefore, to further facilitate zero-resource AUD process, in this paper, we demonstrate acoustic feature representations can be significantly improved by (i) performing linear discriminant analysis (LDA) in an unsupervised self-trained fashion, and (ii) leveraging resources of other languages through building a multilingual bottleneck (BN) feature extractor to give effective cross-lingual generalization. Moreover, we perform comprehensive evaluations of AUD efficacy on multiple downstream speech applications, and their correlated performance suggests that AUD evaluations are feasible using different alternative language resources when only a subset of these evaluation resources can be available in typical zero resource applications.Comment: 5 pages, 1 figure; Accepted for publication at ICASSP 201

Histogram equalization for robust text-independent speaker verification in telephone environments

Word processed copy. Includes bibliographical references

A study of prior sensitivity for Bayesian predictive classificationbased robust speech recognition

We previously introduced a new Bayesian predictive classification (BPC) approach to robust speech recognition and showed that the BPC is capable of coping with many types of distortions. We also learned that the efficacy of the BPC algorithm is influenced by the appropriateness of the prior distribution for the mismatch being compensated. If the prior distribution fails to characterize the variability reflected in the model parameters, then the BPC will not help much. We show how the knowledge and/or experience of the interaction between the speech signal and the possible mismatch guide us to obtain a better prior distribution which improves the performance of the BPC approach.published_or_final_versio

A study of prior sensitivity for Bayesian predictive classificationbased robust speech recognition

We previously introduced a new Bayesian predictive classification (BPC) approach to robust speech recognition and showed that the BPC is capable of coping with many types of distortions. We also learned that the efficacy of the BPC algorithm is influenced by the appropriateness of the prior distribution for the mismatch being compensated. If the prior distribution fails to characterize the variability reflected in the model parameters, then the BPC will not help much. We show how the knowledge and/or experience of the interaction between the speech signal and the possible mismatch guide us to obtain a better prior distribution which improves the performance of the BPC approach.published_or_final_versio