Improved spoken term detection by discriminative training of acoustic models based on user relevance feedback

In a previous paper [1], we proposed a new framework for spoken term detection by exploiting user relevance feedback information to estimate better acoustic model parameters to be used in rescoring the spoken segments. In this way, the acoustic models can be trained with a criterion of better retrieval performance, and the retrieval performance can be less dependent on the existence of a set of acoustic models well matched to the corpora to be retrieved. In this paper, a new set of objective functions for acoustic model training in the above framework was proposed considering the nature of retrieval process and its performance measure, and discriminative training algorithms maximizing the objective functions were developed. Significant performance improvements were obtained in preliminary experiments.