Grammatical word graph re-generation for spontaneous speech recognition

We propose a novel recognition method for generating an accurate grammatical word-graph allowing grammatical deviations. Our method uses both an n-gram and a grammar-based statistical language model and aligns utterances with the grammar by adding deviation information during the search process. Our experiments confirm that the word-graph obtained by our proposed method is superior to the one obtained by only using the n-gram with the same word-graph density. In addition, our recognition method can search enormous hypotheses more efficiently than the conventional wordgraph based search method.

[1]  Hitoshi Iida,et al.  A speech and language database for speech translation research , 1994, ICSLP.

[2]  Wayne H. Ward.  Understanding spontaneous speech: the Phoenix system , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

[3]  Yoshinori Sagisaka,et al.  Variable-order N-gram generation by word-class splitting and consecutive word grouping , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[4]  Alon Lavie,et al.  Glr*: a robust grammar-focused parser for spontaneously spoken language , 1996 .

[5]  Yoshinori Sagisaka,et al.  Spontaneous dialogue speech recognition using cross-word context constrained word graphs , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[6]  Hajime Tsukada,et al.  Integration of grammar and statistical language constraints for partial word-sequence recognition , 1997, EUROSPEECH.

[7]  Yumi Wakita,et al.  Correct parts extraction from speech recognition results using semantic distance calculation, and its application to speech translation , 1997, Workshop On Spoken Language Translation.

[8]  Hermann Ney,et al.  Word graphs: an efficient interface between continuous-speech recognition and language understanding , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Hitoshi Iida,et al.  Example-based error recovery method for speech translation: repairing sub-trees according to the semantic distance , 1998, ICSLP.

[10]  Hitoshi Iida,et al.  A Method for Correcting Errors in Speech Recognition Using the Statistical Features of Character Co-occurrence , 2022, International Conference on Computational Linguistics.

[11]  Victor Zue,et al.  Language modelling for recognition and understanding using layered bigrams , 1992, ICSLP.

[12]  Atsushi Nakamura,et al.  Japanese speech databases for robust speech recognition , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[13]  Tsuyoshi Morimoto,et al.  Dialogue speech recognition using syntactic rules based on subtrees and preterminal bigrams , 1997, Systems and Computers in Japan.