Robust numeric recognition in spoken language dialogue

被引：7
|
作者：
Rahim, M [1 ]
Riccardi, G [1 ]
Saul, L [1 ]
Wright, J [1 ]
Buntschuh, B [1 ]
Gorin, A [1 ]
机构：
[1] AT&T Labs Res, Florham Pk, NJ 07932 USA
来源：
SPEECH COMMUNICATION | 2001年 / 34卷 / 1-2期
关键词：
robustness; spoken dialogue system; speech recognition; utterance verification; discriminative training; understanding; language modeling; numeric recognition; digits;
D O I：
10.1016/S0167-6393(00)00054-6
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
This paper addresses the problem of automatic numeric recognition and understanding in spoken language dialogue. We show that accurate numeric understanding in fluent unconstrained speech demands maintaining robustness at several different levels of system design, including acoustic, language, understanding and dialogue. We describe a robust system for numeric recognition and present algorithms for feature extraction, acoustic and language modeling, discriminative training, utterance verification and numeric understanding and validation. Experimental results from a field-trial of a spoken dialogue system are presented that include customers' responses to credit card and telephone number requests. (C) 2001 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：195 / 212
页数：18
相关论文
共 50 条
  • [11] Recent trends in spoken language dialogue systems
    Minker, W.
    Albalate, A.
    Buehler, D.
    Pittermann, A.
    Pittermann, J.
    Strauss, P. -M.
    Zaykovskiy, D.
    INFORMATION PROCESSING IN THE SERVICE OF MANKIND AND HEALTH, 2006, : 693 - +
  • [12] Spoken Language Understanding for a Nutrition Dialogue System
    Korpusik, Mandy
    Glass, James
    IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2017, 25 (07) : 1450 - 1461
  • [13] Recognition of emotional states in spoken dialogue with a robot
    Komatani, K
    Ito, R
    Kawahara, T
    Okuno, HG
    INNOVATIONS IN APPLIED ARTIFICIAL INTELLIGENCE, 2004, 3029 : 413 - 423
  • [14] Robust spoken Language Identification using Large Vocabulary Speech Recognition.
    Hieronymus, JL
    Kadambe, S
    1997 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I - V: VOL I: PLENARY, EXPERT SUMMARIES, SPECIAL, AUDIO, UNDERWATER ACOUSTICS, VLSI; VOL II: SPEECH PROCESSING; VOL III: SPEECH PROCESSING, DIGITAL SIGNAL PROCESSING; VOL IV: MULTIDIMENSIONAL SIGNAL PROCESSING, NEURAL NETWORKS - VOL V: STATISTICAL SIGNAL AND ARRAY PROCESSING, APPLICATIONS, 1997, : 1111 - 1114
  • [15] Designing a spoken language interface for a tutorial dialogue system
    Bell, Peter
    Dzikovska, Myroslava
    Isard, Amy
    13TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2012 (INTERSPEECH 2012), VOLS 1-3, 2012, : 1282 - 1285
  • [16] Generating Body Motions using Spoken Language in Dialogue
    Ishii, Ryo
    Katayama, Taichi
    Higashinaka, Ryuichiro
    Tomita, Junji
    18TH ACM INTERNATIONAL CONFERENCE ON INTELLIGENT VIRTUAL AGENTS (IVA'18), 2018, : 87 - 92
  • [17] Evaluation and usability of multimodal spoken language dialogue systems
    Dybkjær, L
    Bernsen, NO
    Minker, W
    SPEECH COMMUNICATION, 2004, 43 (1-2) : 33 - 54
  • [18] The integration of the Hungarian language in to the Slovak Spoken dialogue system
    Ondas, Stanislav
    Juhar, Jozef
    Papco, Marek
    Trnka, Marian
    Kiraly, Vojtech
    PROCEEDINGS OF THE 9TH WSEAS INTERNATIONAL CONFERENCE ON SIGNALS, SPEECH AND IMAGE PROCESSING/9TH WSEAS INTERNATIONAL CONFERENCE ON MULTIMEDIA, INTERNET & VIDEO TECHNOLOGIES, 2009, : 102 - +
  • [19] SCALABLE LANGUAGE MODEL ADAPTATION FOR SPOKEN DIALOGUE SYSTEMS
    Gandhe, Ankur
    Rastrow, Ariya
    Hoffmeister, Bjorn
    2018 IEEE WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGY (SLT 2018), 2018, : 907 - 912
  • [20] Sequential Dialogue Context Modeling for Spoken Language Understanding
    Bapna, Ankur
    Tur, Gokhan
    Hakkani-Tur, Dilek
    Heck, Larry
    18TH ANNUAL MEETING OF THE SPECIAL INTEREST GROUP ON DISCOURSE AND DIALOGUE (SIGDIAL 2017), 2017, : 103 - 114
12345