Detecting System-directed Utterances using Dialogue-level Features
نویسندگان
چکیده
We have developed a method to determine whether a user utterance is directed at the system or not. A spoken dialogue system should not respond to audio inputs that are not directed at it (i.e., a user’s mutter), and it therefore needs to detect such inputs to avoid unsuitable responses. We classify the two cases by logistic regression based on a feature set including utterance timing, utterance length, and dialogue status. We conducted experiments using 5395 user utterances for both transcription and automatic speech recognition results. Results showed that the classification accuracy improved by 11.0 and 4.1 points, respectively. We also discuss which features are effective in the classification.
منابع مشابه
A SVM Cascade for Agreement/Disagreement Classification
This article describes a method for classifying dialogue utterances and detecting the interlocutor’s agreement or disagreement. This labelling can help improve dialogue management by providing additional information on the utterance’s content without deep parsing. The proposed technique improves upon state of the art approaches by using a Support Vector Machine cascade. A combination of three b...
متن کاملOnline Error Detection of Barge-In Utterances by Using Individual Users' Utterance Histories in Spoken Dialogue System
We develop a method to detect erroneous interpretation results of user utterances by exploiting utterance histories of individual users in spoken dialogue systems that were deployed for the general public and repeatedly utilized. More specifically, we classify barge-in utterances into correctly and erroneously interpreted ones by using features of individual users’ utterance histories such as t...
متن کاملConstruction of Back-Channel Utterance Corpus for Responsive Spoken Dialogue System Development
In spoken dialogues, if a spoken dialogue system does not respond at all during user’s utterances, the user might feel uneasy because the user does not know whether or not the system has recognized the utterances. In particular, back-channel utterances, which the system outputs as voices such as“yeah”and“uh huh”in English have important roles for a driver in in-car speech dialogues because the ...
متن کاملMachine Learning for Shallow Interpretation of User Utterances in Spoken Dialogue Systems
We investigate to what extent automatic learning techniques can be used for shallow interpretation of user utterances in spoken dialogue systems. This task involves dialogue act classification, shallow understanding and problem detection simultaneously. For this purpose we train both a rule-induction and a memory-based learning algorithm on a large set of surface features obtained by affordable...
متن کاملSpeakers' Intention Prediction Using Statistics of Multi-level Features in a Schedule Management Domain
Speaker’s intention prediction modules can be widely used as a pre-processor for reducing the search space of an automatic speech recognizer. They also can be used as a preprocessor for generating a proper sentence in a dialogue system. We propose a statistical model to predict speakers’ intentions by using multi-level features. Using the multi-level features (morpheme-level features, discourse...
متن کامل