Keyword recognition and extraction by multiple-LVCSRs with 60, 000 words in speech-driven WEB retrieval task

This paper presents speech-driven Web retrieval models which accepts spoken search topics (queries) in the NTCIR-3 Web retrieval task. We experimentally evaluate the techniques of combining outputs of multiple LVCSR models with a language model(LM) with a 60,000 vocabulary size in recognition of spoken queries. As model combination techniques, we use the SVM learning. We show that the techniques of multiple LVCSR model combination can achieve improvement both in speech recognition and retrieval accuracies in speech-driven text retrieval. Comparing with the retrieval accuracies when a LM with a 20,000/60,000 vocabulary size is used in LVCSRs, the LM that has larger size of the vocabulary improves also retrieval accuracies.