Automatic modeling of user specific words for a speaker independent recognition system

The problem addressed in this paper, is the incorporation of user specific words in a speaker independent speech recognition system. No transcription is used to model the new words, modeling is based on a very small number of training utterances only. We investigated two different modeling methods. The first is intended for small vocabulary recognisers. The HMM models for the new words are enhanced by averaging their states with the nearest speaker independent state. This way, the recognition error was reduced by a factor two, and even the noise robustness of the speaker independent models seems to be transferred to the new models. The second method can be used in large vocabulary recognisers. Using a CSR algorithm, a transcription for the new words is found in terms of the subword models in the recogniser. The resulting models perform equally well as the models based on phonetic transcriptions