Linguistic Processing Using a Dependency Structure Grammar for Speech Recognition and Understanding

LINGUISTIC PROCESSING USING A DEPENDENCY STRUCTURE GRAMMAR FOR SPEECH RECOGNITION AND UNDERSTANDING Sho-lchl MATSUNAGA NTT Human Interface Laboratories Musashino, To;¢yo, 180, Japan and Masaki KOHDA University of Yamagata Yonezawa, Yamagata, 922, Japan Abstract This paper proposes an efficient linguistic processing strategy for speech recognition and understanding using a dependency structure grammar. The strategy includes parsing and phrase prediction algorithms. After speech processing and phrase recognition based on phoneme recognition, the parser extracts the sentence with the best likelihood taking account of the phonetic likelihood of phrase candidates and the linguistic likelihood of the semantic inter-phrase dependency relationships. A fast parsing algorithm using breadth-first search is also proposed. The predictor pre-selects the p}~.ase candidates using transition rules combined with a dependency structure to reduce the amount of phonetic processing. The proposed linguistic processor has been tested through speech recognition experiments. The experimental results show that it greatly increases the accuracy of speech recognitions, and the breadth-first parsing algorithm and predictor increase processing speed. 1. Introduction In conventional continuous speech recognition and understanding systems[1~4], linguistic rules for sentences composed of phrase sequences are usually expressed by a phrase structure grammar such as a transition network or context free grammar. In such methods, however, phoneme recognition errors and rejections result in incorrect transition states because of the strong syntactical constraints. These erroneous transitions cause the following candidates to be incorrectly chosen or the processing system to halt. Therefore, these errors and rejections can be fatal to speech understanding. Furthermore, a complete set of these grammatical rules for speech understanding is very difficult to provide. To address these problems, this paper proposes a new linguistic processor based on a dependency structure grammar, which integrates a bottom~up sentence parser and a top-down phrase predictor. ~lis grammar is more semantic and less syntactic than phrase structure grammar, and, therefore, syntactic positional constraint in a sentence rarely occurs with this parser. This effectively prevents extreme degradation in speech recognition from errors and rejections in phoneme recognition and greatly increases the accuracy of speech processing. This grammar only has two syntactic rules, so this parser is free of many cumbersome grammatical rules that are indispensable to other grammars. This grammar particularly suits phrase-order-free languages such as Japanese. 4a~ rule I rule 2 For the parser of this grammar, a depth-first parsing algorithm with backtracking which guarantees the optimal solution was devised[5]. However, parsing long sentences composed of many phrases with this algorithm can be time-consuming because of combinatorial explosion, since the amount of computation is exponential order with respect to the number of phrases. Therefore, a fast parsing algorithm using breadth-first search and beam search was developed. This algorithm is based on flmdamental algorithms[6,7] which only take account of the dependency relationships of the modifier and modificant phrases, and it handles higher linguistic or semantic processing such as case structure. The processing ability of this breadth-first algorithm is equivalent to that of the depth-first algorithm. To effectively recognize speech, the amount of phonetic processing must be reduced through …

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