Yuichi Ohkawa

Aspect-model-based reference speaker weighting

We propose an aspect-model-based reference speaker weighting. The main idea of the approach is that the adapted model is a linear combination of a set of reference speakers like reference speaker weighting (RSW) and eigenvoices. The aspect model is the mixture model of speaker-dependent (SD) models. In this paper, aspect model weighting (AMW) is proposed for finding an optimal weighting of a set of reference speakers unlike RSW and the aspect model which is a kind of cluster models is trained based on likelihood maximization with respect to the training data. The number of adaptation parameters can also be reduced using aspect model approach. For evaluation, we carried out an isolated word recognition experiment on Korean database (KLE452). The results were compared to those of conventional MAP, MLLR, RSW, and eigenvoice. Even though we use only 0.5s of adaptation data, 27.24% relative error rate reduction in comparison with speaker-independent (SI) baseline performance was achieved.

A phoneme duration model considering speaking‐rate and linguistic features for speech recognition

In this paper, we proposed a method of phoneme duration modeling for speech recognition. A phoneme with extremely short or long duration often causes a decline of performance of speech recognition. In order to improve performance of recognition, an estimation of phoneme duration determined by various parameters is required. However, there was no usual method of duration modeling for speech recognition considering the influence of both speaking‐rate and linguistic feature (phoneme location in sentence, part‐of‐speech, et al.), which influence phoneme duration strongly. Therefore, we modeled influence of speaking‐rate by two‐dimensional normal distribution of phoneme duration and local average of vowel duration. Each normal distribution is determined by tree‐based clustering with various questions, which include linguistic feature. With an experiment of estimation of phoneme duration by this model, we acquired 20.8% reduction of standard deviation of estimation error. We also used the proposed duration mode...