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Miscommunication handling in spoken dialog systems based on error-aware dialog state detection

EURASIP journal on audio, speech, and music processing, 2017-05, Vol.2017 (1), p.1-17, Article 9 [Peer Reviewed Journal]

The Author(s). 2017 ;EURASIP Journal on Audio, Speech, and Music Processing is a copyright of Springer, 2017. ;ISSN: 1687-4722 ;ISSN: 1687-4714 ;EISSN: 1687-4722 ;DOI: 10.1186/s13636-017-0107-3

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  • Title:
    Miscommunication handling in spoken dialog systems based on error-aware dialog state detection
  • Author: Wu, Chung-Hsien ; Su, Ming-Hsiang ; Liang, Wei-Bin
  • Subjects: Acoustics ; Automatic speech recognition ; Deletion ; Engineering ; Engineering Acoustics ; Error detection ; Error-aware dialog act ; Handling ; Mathematics in Music ; Miscommunication ; Recovery ; Semantics ; Sentences ; Signal,Image and Speech Processing ; Spoken dialog systems ; Voice recognition ; Voice response technology
  • Is Part Of: EURASIP journal on audio, speech, and music processing, 2017-05, Vol.2017 (1), p.1-17, Article 9
  • Description: With the exponential growth in computing power and progress in speech recognition technology, spoken dialog systems (SDSs) with which a user interacts through natural speech has been widely used in human-computer interaction. However, error-prone automatic speech recognition (ASR) results usually lead to inappropriate semantic interpretation so that miscommunication happens easily. This paper presents an approach to error-aware dialog state ( DS ) detection for robust miscommunication handling in an SDS. Non-understanding ( Non-U ) and misunderstanding ( Mis-U ) are considered for miscommunication handling in this study. First, understanding evidence (UE), derived from the recognition confidence, is adopted for Non-U detection followed by Non-U recovery. For Mis-U with the recognized sentence containing uncertain recognized words, the partial sentences obtained by removing potentially misrecognized words from the input utterance are organized, based on regular expressions, as a tree structure to tolerate the deletion or rejection of keywords resulting from misrecognition for Mis-U DS modeling. Latent semantic analysis is then employed to consider the verified words and their n -grams for DS detection, including Mis-U and predefined Base DSs. Historical information-based n -grams are employed to find the most likely DS for the SDS. Several experiments were performed with a dialog corpus for the restaurant reservation task. The experimental results show that the proposed approach achieved a promising performance for Non-U recovery and Mis-U repair as well as a satisfactory task success rate for the dialogs using the proposed method.
  • Publisher: Cham: Springer International Publishing
  • Language: English
  • Identifier: ISSN: 1687-4722
    ISSN: 1687-4714
    EISSN: 1687-4722
    DOI: 10.1186/s13636-017-0107-3
  • Source: GFMER Free Medical Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
    Springer Nature OA Free Journals
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