On-line detection of task incompletion for spoken dialog systems using utterance and behavior tag N-gram vectors

Hara, Sunao; Kitaoka, Norihide; Takeda, Kazuya

doi:10.1007/978-1-4614-1335-6_22

Cited by 1 publication

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“…Most phrases for the correction can only be mapped to the desired DS that contains only one argument. Based on these observations, historical information is crucial to improve the performance of a dialog system [40,44]. Figure 6 shows the block diagram of the proposed SDS.…”

Section: Dialog State Detectionmentioning

confidence: 99%

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

Liang

2017

J AUDIO SPEECH MUSIC PROC.

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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.

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Section: Dialog State Detectionmentioning

confidence: 99%

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

Liang

2017

J AUDIO SPEECH MUSIC PROC.

View full text Add to dashboard Cite

show abstract

On-line detection of task incompletion for spoken dialog systems using utterance and behavior tag N-gram vectors

Cited by 1 publication

References 7 publications

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

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

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