This paper presents a new approach to online adaptation of continuous density hidden Markov model (CDHMM) with a small amount of adaptation data based on speaker space model (SSM) evolution. The SSM which characterizes the a priori knowledge of the training speakers is effectively described in terms of the latent variable models such as the factor analysis or probabilistic principal component analysis. The SSM provides various sources of information such as the correlation information, the prior density, and the prior knowledge of the CDHMM parameters that are very useful for rapid online adaptation. We design the SSM evolution based on the quasi-Bayes estimation technique which incrementally updates the hyperparameters of the SSM and the CDHMM parameters simultaneously. In a series of speaker adaptation experiments on the continuous digit and large vocabulary recognition tasks, we demonstrate that the proposed approach not only achieves a good performance for a small amount of adaptation data but also maintains a good asymptotic convergence property as the data size increases.
['Dong Kook Kim', 'Nam Soo Kim']
Rapid online adaptation using speaker space model evolution