Hierarchical Stochastic Feature Matching For Robust Speech Recognition

In this paper we investigate how to improve the robustness of a speech recognizer in a noisy, mismatched environment when only a single or a few test utterances are available for compensating the mismatch. A new hierarchical tree-based transformation is proposed to enhance the conventional stochastic matching algorithm in the cepstral feature space. The tree-based hierarchical transformation is estimated in two criteria: i) maximum likelihood (ML) using the current test utterance; ii) Sequential maximum a posterior (MAP) using the current and previous utterances. Recognition results obtained using a hands-free database show the proposed feature compensation is robust. Significant performance improvement has been observed over the conventional stochastic matching.