SPEECH UNDERSTANDING BASED ON KEYWORD EXTRACTION AND RELATIONS BETWEEN WORDS

In this paper, we propose a method for speech understanding using a corpus. First, the method extracts keywords from an N‐best list of speech recognizer output. This process employs two measures: a confidence measure of speech recognizer output and an association probability between words. Next, the method uses dependencies between keywords in a corpus. Finally, it evaluates relations (tagged labels) of the dependencies. Our method obtained high accuracy as compared with a method with only the confidence measure of the speech understanding module. The results show the effectiveness of the proposed method.

[1]  Genichiro Kikui,et al.  Applying example-based error correction selectively , 2003, 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721).

[2]  Jeung-Yoon Choi,et al.  Simultaneous recognition of words and prosody in the Boston University Radio Speech Corpus , 2005, Speech Commun..

[3]  Renato De Mori,et al.  The Application of Semantic Classification Trees to Natural Language Understanding , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Hiroki Arimura,et al.  Efficient Substructure Discovery from Large Semi-Structured Data , 2001, IEICE Trans. Inf. Syst..

[5]  Diana Inkpen,et al.  Semantic Similarity for Detecting Recognition Errors in Automatic Speech Transcripts , 2005, HLT.

[6]  Tsuneo Kagawa,et al.  Cooperative Understanding of Utterances and Gestures in a Dialogue‐Based Problem Solving System , 1999, Comput. Intell..

[7]  Tatsuya Kawahara,et al.  Confirmation strategy for document retrieval systems with spoken dialog interface , 2004, INTERSPEECH.

[8]  Katsuhito Sudoh,et al.  Incorporating discourse features into confidence scoring of intention recognition results in spoken dialogue systems , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[9]  Kiyohiro Shikano,et al.  Julius - an open source real-time large vocabulary recognition engine , 2001, INTERSPEECH.

[10]  Frédéric Béchet,et al.  On the Use of Confidence for Statistical Decision in Dialogue Strategies , 2004, SIGDIAL Workshop.

[11]  Sadaoki Furui,et al.  Language Models and Dialogue Strategy for a Voice QA System , 2004 .

[12]  Andrew Sears,et al.  Applying the Naïve Bayes Classifier to Assist Users in Detecting Speech Recognition Errors , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[13]  Yasuyuki Kono,et al.  BTH: an efficient parsing algorithm for word-spotting , 1998, ICSLP.

[14]  Nigel Gilbert,et al.  Simulating speech systems , 1991 .

[15]  Tatsuya Kawahara,et al.  Large Vocabulary Continuous Speech Recognition Parser based on A* Search using Grammar Category - Pair Constraint , 1998 .

[16]  Tatsuya Kawahara,et al.  Flexible Mixed-Initiative Dialogue Management using Concept-Level Confidence Measures of Speech Recognizer Output , 2000, COLING.

[17]  Naoyuki Kanda,et al.  Contextual constraints based on dialogue models in database search task for spoken dialogue systems , 2005, INTERSPEECH.

[18]  Takenobu Tokunaga,et al.  Evaluation of a robust parser for spoken Japanese , 2003, DiSS.

[19]  Masataka Goto,et al.  Speech repair: quick error correction just by using selection operation for speech input interfaces , 2005, INTERSPEECH.

[20]  Lou Boves,et al.  Incorporating confidence measures in the Dutch train timetable information system developed in the ARISE project , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[21]  Ian R. Lane,et al.  Verification of Speech Recognition Results Incorporating In-domain Confidence and Discourse Coherence Measures , 2006, IEICE Trans. Inf. Syst..

[22]  Mikio Nakano,et al.  Understanding Unsegmented User Utterances in Real-Time Spoken Dialogue Systems , 1999, ACL.