Robust speech understanding against inter-speaker variation and ungrammatical utterances based on high-accuracy speech recognition and semantic driven parsing method

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The aim of this research is to construct a robust speech understanding system against inter-speaker variation and ungrammatical utterances. In order to implement such robust system, we develop a high accuracy speech recognizer with a speaker adaptation method and a semantic driven parsing method.1.Speaker adaptation of HMM phoneme recognizerWe develop a speaker adaptation method using continuous speech input against inter-speaker variation. We use maximum a posteriori probability estimation to Continuous density Hidden Markov Model (HMM) based on Pair-Wise Bays Classifiers as the phone classifier. We performed experimental evaluation of adaptation to 8 speakers. As a result, the keywords recognition rate of the adapted model of a speaker reached 80.2 %, Which is higher by 11.0 % than that of the baseline model, while the accuracy is lowered for another speaker.2.Word/Phrase spotting methodEven in spontaneous speech, most words and phrases are correctly uttered. Then, we need a word/phrase spotting method for robust parsing. In order to increase accuracy of these spotter, we develop a heuristic language model that models the rest of target word/phrase. Also, we implement a island-driven praser that can skip filled pauses and unknown words.Robust speech parser by incremental analysisIn natural dialogues, fragmentary utterances are frequently used. Existing approach can hardly deal with these phenomena because it presupposes a complete sentence input. We try to use incremental parsing method with relaxation to such fragmentary utterances. For implementation, we use marker passer to integrate input fragment to recognized plan structure.