抄録
Nowadays the voice user interface using the automatic speech recognition (ASR) is used for car navigation, tourist's machine translation and information retrieval on the smart phone. Tasks of these applications are well-defined and speakers' high motivation makes them speak clearly. The full equipped domain-limited language models and the clear speaking contribute to the high accurate speech recognition. But in casual conversations, the dialogue domains are not limited and speakers' utterances are not grammatical. This is an ill-defined task for N-gram language model. Latent semantic analysis (LSA) is a technique that can cover long range semantic coherence with semantic relationship between two words in the recognition results. It does not take a word-order into account, so that it is applicable for ungrammatical utterances. It would appear to be effective to use two words with high co-occurrence as correct results, but there is a problem in that a pair of two misrecognized words may have a high co-occurrence. Since a language model such as N-gram applies short range semantic similarities, word pairs with high co-occurrence are frequently recognized together. Our method is a modified LSA. We propose using a word co-occurrence analysis with utterance pairs in order to obtain the appropriate keywords. In a conversation, because speakers talk about one common domain, a user's utterance and a system's last utterance may have high co-occurrence. The system reacts to the recognized words that have high co-occurrence with words in the system's last utterance. We applied this word co-occurrence analysis with utterance pairs to our voice interface robot. The precision rate was 43% in the original recognition system and it was improved to 71% with the word co-occurrence analysis.
