## First International Conference on Spoken Language Processing (ICSLP 90)## Kobe, Japan |

As an speaker adaptation method of continuous parameter HMM, we adapted mean vectors which are a part of parameters of multi-dimensional normal distributions. We regard a set of mean vectors belonging to each HMM as a codebook. The unsupervised adaptation algorithm modifies the mean vectors by using vector-quantization error-vectors for a test speaker. For two persons, 23 Japanese phoneme recognition accuracy was improved from 62% of non-adaptation into 73% after unsupervised adaptation and into 82% after supervised adaptation, respectively. Also, we describe adaptation results through multi-speaker mode HMM and comparison between the improvement by speaker adaptation and the degradation by vector-quantization of input vectors on speaker dependent mode HMM.

__Bibliographic reference.__
Hirata, Yoshimitsu / Nakagawa, Seiichi (1990):
"Speaker adaptation of continuous parameter HMM",
In *ICSLP-1990*, 377-380.