Discriminative acoustic model using eigenspace mapping for rapid speaker adaptation

It is widely believed that strong correlations exist across an utterance as a consequence of time-invariant characteristics of speaker and acoustic environments. It is verified in this paper that the first primary eigendirections of the utterance covariance matrix are speaker dependent. Based on this observation, a fast speaker adaptation algorithm entitled Eigenspace Mapping (EigMap) is proposed and described. EigMap rapidly adapts the speaker independent models by constructing discriminative acoustic models in the test speaker´s eigenspace. Unsupervised adaptation experiments show that EigMap is effective in improving baseline models using very limited amounts of adaptation data with superior performance to conventional adaptation technique such as block diagonal MLLR. A relative improvement of 18.4% over baseline recognizer is achieved using EigMap with only about 4.5 seconds of adaptation data. It is also demonstrated that EigMap is additive to MLLR by encompassing the speaker dependent discrimination information. A significant relative improvement of 24.6% over baseline is observed by combining MLLR and EigMap techniques.