CROSS-LINGUAL CONTEXT SHARING AND PARAMETER-TYING FOR MULTI-LINGUAL SPEECH RECOGNITION

This paper is concerned with the problem of building acoustic models for automatic speech recognition (ASR) using speech data from multiple languages. Techniques for multi-lingual ASR are developed in the context of the subspace Gaussian mixture model (SGMM)[2, 3]. Multi-lingual SGMM based ASR systems have been configured with shared subspace parameters trained from multiple languages but with distinct language dependent phonetic contexts and states[11, 12]. First, an approach for sharing state-level target language and foreign language SGMM parameters is described. Second, semi-tied covariance transformations are applied as an alternative to full-covariance Gaussians to make acoustic model training less sensitive to issues of insufficient training data. These techniques are applied to Hindi and Marathi language data obtained for an agricultural commodities dialog task in multiple Indian languages.