A Nonparametric Bayesian Approach for Spoken Term detection by Example Query
This work addresses the challenge of speech processing for low-resource languages where large training datasets are unavailable, offering a method for automatic acoustic unit discovery.
The paper tackles the problem of spoken term detection in low-resource languages by proposing a nonparametric Bayesian model for acoustic unit discovery, achieving a P@N of 61.2% and an EER of 13.95% on the TIMIT dataset, with a 5% improvement in EER.
State of the art speech recognition systems use data-intensive context-dependent phonemes as acoustic units. However, these approaches do not translate well to low resourced languages where large amounts of training data is not available. For such languages, automatic discovery of acoustic units is critical. In this paper, we demonstrate the application of nonparametric Bayesian models to acoustic unit discovery. We show that the discovered units are correlated with phonemes and therefore are linguistically meaningful. We also present a spoken term detection (STD) by example query algorithm based on these automatically learned units. We show that our proposed system produces a P@N of 61.2% and an EER of 13.95% on the TIMIT dataset. The improvement in the EER is 5% while P@N is only slightly lower than the best reported system in the literature.