Welcome to Journal of University of Chinese Academy of Sciences,Today is

Design and implementation of mandarin spoken dialogue system for flight reservation

  • CHEN Zhenfeng ,
  • YANG Xiaohao ,
  • WU Weilan ,
  • LIU Jia ,
  • XIA Shanhong
Expand
  • 1. State Key Laboratory on Transducing Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China;
    2. University of Chinese Academy of Sciences, Beijing 100190, China;
    3. Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Received date: 2014-03-24

  Revised date: 2014-05-16

  Online published: 2015-03-15

Abstract

We present a spoken dialogue system for flight reservation, which allows users to inquire information about flight in mandarin. We describe the design and the implementation of our system, focusing on spoken language understanding (SLU). Considering that the speech recognizer inevitably makes errors, we propose a new two-stage mandarin SLU approach based on word confusion network. Firstly, the semantic tuple classifier is used to identify the topic of an input utterance using N-gram features extracted from the word confusion network and to parse a semantic tree by recursively calling semantic classification models. Then the rule-based semantic slot filler is used to extract the corresponding slot/value pairs. The advantage of the proposed approach is that it is mainly data-driven and requires minimally annotated corpus for training. Experiment has been carried out in the Chinese flight reservation domain, which shows that the proposed approach is more robust to speech recognition errors than the conventional handcrafted rule-based parser, and substantially improves performance of accuracy when the ASR word error rate is high.

Cite this article

CHEN Zhenfeng , YANG Xiaohao , WU Weilan , LIU Jia , XIA Shanhong . Design and implementation of mandarin spoken dialogue system for flight reservation[J]. Journal of University of Chinese Academy of Sciences, 2015 , 32(2) : 252 -258 . DOI: 10.7523/j.issn.2095-6134.2015.02.015

References

[1] Weng F, Cavedon L, Raghunathan B, et al. A conversational dialogue system for cognitively overloaded users [C]//International Conference on Spoken Language Processing. 2004.

[2] Liu J, Xu Y, Seneff S, et al. CityBrowser II: a multimodal restaurant guide in mandarin [C]//International Symposium on Chinese Spoken Language Processing. 2008: 1-4.

[3] Huang C, Xu P, Zhang X, et al. LODESTAR: a mandarin spoken dialogue system for travel information retrieval [C]//Proceedings of Eurospeech. 1999, 99: 1 159-1 162.

[4] 黄寅飞, 郑方,燕鹏举,等. 校园导航系统 EasyNav 的设计与实现[J]. 中文信息学报, 2001, 15(4): 35-40.

[5] ?ibert J, Martin ?i ?-Ipši ? S, Hajdinjak M, et al. Development of a bilingual spoken dialog system for weather information retrieval [C]//Proceedings of Eurospeech. 2003: 1 917-1 920.

[6] Lin Y C, Chiang T H, Wang H M, et al. The design of a multi-domain mandarin Chinese spoken dialogue system [C]//International Conference on Spoken Language Processing. 1998.

[7] Ward W H. The Phoenix system: understanding spontaneous speech [C]//Proceedings of ICASSP. 1991, 66.

[8] Ward W, Issar S. Recent improvements in the CMU spoken language understanding system [C]//Proceedings of the workshop on Human Language Technology. Association for Computational Linguistics, 1994: 213-216.

[9] Oerder M, Ney H. Word graphs: an efficient interface between continuous-speech recognition and language understanding [C]//Proceedings of ICASSP. 1993, 2: 119-122.

[10] Tür G, Wright J H, Gorin A L, et al. Improving spoken language understanding using word confusion networks [C]//Interspeech. 2002.

[11] Mangu L, Brill E, Stolcke A. Finding consensus among words: lattice-based word error minimization [C]//Proceedings of Eurospeech. 1999.

[12] Hakkani-Tür D, Béchet F, Riccardi G, et al. Beyond ASR 1-best: using word confusion networks in spoken language understanding[J]. Computer Speech & Language, 2006, 20(4): 495-514.

[13] Mairesse F, Gasic M, Jurcícek F, et al. Spoken language understanding from unaligned data using discriminative classification models [C]//Processing of ICASSP. 2009: 4 749-4 752.

[14] Chang C C, Lin C J. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2011, 2(3): 27.

[15] Lee C, Jung S, Kim K, et al. Recent approaches to dialog management for spoken dialog systems[J]. JCSE, 2010, 4(1): 1-22

[16] Cole R. Tools for research and education in speech science [C]//Proceedings of the International Conference of Phonetic Sciences. 1999: 1 277-1 280.

[17] Aust H, Schroer O. An overview of the Philips dialog system [C]//DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, VA. 1998.

[18] Bos J, Klein E, Lemon O, et al. DIPPER: Description and formalisation of an information-state update dialogue system architecture [C]//4th SIGdial Workshop on Discourse and Dialogue. 2003: 115-124.

[19] Ljunglöf P. trindikit. py: an open-source Python library for developing ISU-based dialogue systems[J]. Proceedings of IWSDS, 2009, 9.

[20] Rich C, Sidner C L. COLLAGEN: a collaboration manager for software interface agents[J]. User Modeling and User-Adapted Interaction, 1998, 8(3/4): 315-350.

[21] Bohus D, Rudnicky A I. The RavenClaw dialog management framework: architecture and systems[J]. Computer Speech & Language, 2009, 23(3): 332-361.

Outlines

/