This paper describes a novel method for reducing the transcription effort in the construction of task-adapted acoustic models for a practical automatic speech recognition (ASR) system. We have to prepare actual data samples collected in the practical system and transcribe them for training the task-adapted acoustic models. However, transcribing utterances is a time-consuming and laborious process. In the proposed method, we firstly adapt initial models to acoustic environment of the system using a small number of collected data samples with transcriptions. And then, we automatically select informative training data samples to be transcribed from a large-sized speech corpus based on acoustic likelihoods of the models. We perform several experimental evaluations in the framework of “Takemarukun”, a practical speech-oriented guidance system. Experimental results show that 1) utterance sets with low likelihoods cause better task-adapted models compared with those with high likelihoods although the set with the lowest likelihoods causes the performance degradation because of including outliers, and 2) MLLR adaptation is effective for training the task-adapted models when the amount of the transcribed data is small and EM training outperforms MLLR if we transcribe more than around 10,000 utterances.

As a practical information guidance system, we have been developing a speech-oriented system named "Takemaru-kun". The system has been operated on a public space since Nov. 2002. The system answers to user's question about the hall facilities, sightseeing, transportation, weather information around the city, etc. All triggered inputs to the system have been recorded since the operation started. And all system inputs during 22 months are manually transcribed and labelled for speaker’s gender and age category. In this paper, we conduct a long-term prosody analysis of user speech to find a clue to obtain user’s attitude from a user’s speech. In this preliminary analysis, it is observed that F0 decreases regardless of age and gender category when the stability of the dialogue system is not established.