Online Unsupervised Learning of Hmm Parameters for Speaker Adaptation

This paper presents an online unsupervised learning algorithm to flexibly adapt the speaker-independent (SI) hidden Markov models (HMM’s) to new speaker. We apply the quasi-Bayes (QB) estimate to incrementally obtain word sequence and adaptation parameters for adjusting HMM’s once a block of unlabeled data is enrolled. Accordingly, the nonstationary statistics of varying speakers can be successively traced according to the newest enrollment data. To improve the QB estimate, we employ the adaptive initial hyperparameters in the beginning session of online learning. These hyperparameters are estimated from a cluster of training speakers closest to the test speaker. Additionally, we develop a selection process to select reliable parameters from a list of candidates for unsupervised learning. A set of reliability assessment criteria is explored. From the experiments, we confirm the effectiveness of proposed method and find that using the adaptive initial hyperparameters in online learning and the multiple assessments in parameter selection can improve the speaker adaptation performance.