Speaking style adaptation for spontaneous speech recognition using multiple-regression HMM

This paper describes a rapid model adaptation technique for spontaneous speech recognition. The proposed technique utilizes a multiple-regression hidden Markov model (MRHMM) and is based on a style estimation technique of speech. In the MRHMM, the mean vector of probability density function (pdf) is given by a function of a low-dimensional vector, called style vector, which corresponds to the intensity of expressivity of speaking style variation. The value of the style vector is estimated for every utterance of the input speech and the model adaptation is conducted by calculating new mean vectors of the pdf using the estimated style vector. The performance evaluation results using “Corpus of spontaneous Japanese (CSJ)” are shown under a condition in which the amount of model training and adaptation data is very small.