Joint model and feature space optimization for robust speech recognition

This paper presents a maximum likelihood joint-space adaptation technique for robust speech recognition. In the joint-space adaptation process, the N-best hidden Markov model (HMM) inversion frame-by-frame adapts the speech features non-parametrically to compensate the temporal deviation, while the models are transformed parametrically to catch the global characteristics of the mismatch. The proposed joint-space adaptation provides a better compensation to the mismatch than the single-space adaptations. This algorithm operates only on the given testing speech and the models, therefore no adaptation data are required. As verified by the experiments performed under different mismatch environments, the proposed method improves the performance in all the cases without degrading the performance under the match condition