Selective EM training of acoustic models based on sufficient statistics of single utterances

In this paper, a new algorithm for selective training of acoustic models is proposed. The algorithm is formulated for an HMM-based model with Gaussian mixture densities, but works in principle for any statistical model, which has sufficient statistics. Since there are too many possibilities for selecting a data subset from a larger database, a heuristic has to be employed. The algorithm is based on deleting single utterances from a data pool temporarily or alternating between successive deletion or addition of utterances. The optimization criterion is the likelihood of the new model parameters given some development data, which can be calculated in a short amount of time based on sufficient statistics. The method is applied to automatically obtain task-dependent acoustic models for infant and elderly speech by selecting utterances from a data pool which are acoustically close to the development data. The proposed method is computationally practical and also addresses the issue of reducing the high costs evolving from the development of applications which make use of speech recognition technology