Spoken Language Models and Dialog 2
HomeFull List of Titles
1: ICSLP'98 Proceedings
Keynote SpeechesText-To-Speech Synthesis 1Spoken Language Models and Dialog 1Prosody and Emotion 1Hidden Markov Model Techniques 1Speaker and Language Recognition 1Multimodal Spoken Language Processing 1Isolated Word RecognitionRobust Speech Processing in Adverse Environments 1Spoken Language Models and Dialog 2Articulatory Modelling 1Talking to Infants, Pets and LoversRobust Speech Processing in Adverse Environments 2Spoken Language Models and Dialog 3Speech Coding 1Articulatory Modelling 2Prosody and Emotion 2Neural Networks, Fuzzy and Evolutionary Methods 1Utterance Verification and Word Spotting 1 / Speaker Adaptation 1Text-To-Speech Synthesis 2Spoken Language Models and Dialog 4Human Speech Perception 1Robust Speech Processing in Adverse Environments 3Speech and Hearing Disorders 1Prosody and Emotion 3Spoken Language Understanding Systems 1Signal Processing and Speech Analysis 1Spoken Language Generation and Translation 1Spoken Language Models and Dialog 5Segmentation, Labelling and Speech Corpora 1Multimodal Spoken Language Processing 2Prosody and Emotion 4Neural Networks, Fuzzy and Evolutionary Methods 2Large Vocabulary Continuous Speech Recognition 1Speaker and Language Recognition 2Signal Processing and Speech Analysis 2Prosody and Emotion 5Robust Speech Processing in Adverse Environments 4Segmentation, Labelling and Speech Corpora 2Speech Technology Applications and Human-Machine Interface 1Large Vocabulary Continuous Speech Recognition 2Text-To-Speech Synthesis 3Language Acquisition 1Acoustic Phonetics 1Speaker Adaptation 2Speech Coding 2Hidden Markov Model Techniques 2Multilingual Perception and Recognition 1Large Vocabulary Continuous Speech Recognition 3Articulatory Modelling 3Language Acquisition 2Speaker and Language Recognition 3Text-To-Speech Synthesis 4Spoken Language Understanding Systems 4Human Speech Perception 2Large Vocabulary Continuous Speech Recognition 4Spoken Language Understanding Systems 2Signal Processing and Speech Analysis 3Human Speech Perception 3Speaker Adaptation 3Spoken Language Understanding Systems 3Multimodal Spoken Language Processing 3Acoustic Phonetics 2Large Vocabulary Continuous Speech Recognition 5Speech Coding 3Language Acquisition 3 / Multilingual Perception and Recognition 2Segmentation, Labelling and Speech Corpora 3Text-To-Speech Synthesis 5Spoken Language Generation and Translation 2Human Speech Perception 4Robust Speech Processing in Adverse Environments 5Text-To-Speech Synthesis 6Speech Technology Applications and Human-Machine Interface 2Prosody and Emotion 6Hidden Markov Model Techniques 3Speech and Hearing Disorders 2 / Speech Processing for the Speech and Hearing Impaired 1Human Speech ProductionSegmentation, Labelling and Speech Corpora 4Speaker and Language Recognition 4Speech Technology Applications and Human-Machine Interface 3Utterance Verification and Word Spotting 2Large Vocabulary Continuous Speech Recognition 6Neural Networks, Fuzzy and Evolutionary Methods 3Speech Processing for the Speech-Impaired and Hearing-Impaired 2Prosody and Emotion 72: SST Student Day
SST Student Day - Poster Session 1SST Student Day - Poster Session 2Author Index
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An Event Driven Model for Dialogue Systems
Authors:
Kuansan Wang, Microsoft Research (USA)
Page (NA) Paper number 888
Abstract:
This paper reports our progress in building a mixed initiative, goal-oriented dialogue system for human machine interactions. The dialogue model embraces the spirits of the so-called plan-based approach in that the dialogue flow is not statically authored but dynamically generated by the system as a natural outcome of the semantic evaluation process. With multimodal applications in mind, the dialogue system is designed in an event driven architecture that is commonly seen in the core of a graphical user interface (GUI) environment. In the same manner that GUI events are handled by graphical objects, the proposed dialogue model assigns dialogue events to semantic objects that encapsulate the knowledge for handling events under various discourse contexts. Currently, we have found that four types of events, namely, dialogue object instantiation, semantic evaluation, dialogue repair, and discourse binding, are sufficient for a wide range of applications.
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Automatic Classification of Dialogue Contexts for Dialogue Predictions
Authors:
Cosmin Popovici, I.C.I. - Institutul de Cercetari in Informatica (Romania)
Paolo Baggia, CSELT - Centro Studi e Laboratori Telecomunicazioni (Italy)
Pietro Laface, Politecnico di Torino (Italy)
Loreta Moisa, Politecnico di Torino (Italy)
Page (NA) Paper number 552
Abstract:
This paper exploits the broad concept of dialogue predictions by linking a point in a human-machine dialogue with a specific language model which is used during the recognition of the next user utterance. The idea is to cluster several dialogue contexts into a class and to create for each class a specific language model. We present an automatic algorithm based on the minimal decrease of mutual information which clusters the dialogue contexts. Moreover the algorithm is able to guess an appropriate number of classes, that gives a good trade off between the mutual information and the amount of training data. Therefore the automatic classification allows the full automatic creation of context-dependent language models for a spoken dialogue system.
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Automatic Identification of Command Boundaries in a Conversational Natural Language User Interface
Authors:
Ganesh N. Ramaswamy, I.B.M. Research Center (USA)
Jan Kleindienst, I.B.M. Research Center (USA)
Page (NA) Paper number 612
Abstract:
In this paper, we propose a trainable system that can automatically identify the command boundaries in a conversational natural language user interface. The proposed solution makes the conversational interface much more user friendly, and allows the user to speak naturally and continuously in a hands-free manner. The main ingredient of the system is the maximum entropy identification model, which is trained using data that has all the correct command boundaries marked. During training, a set of features and their weights are selected iteratively using the training data. The features consists of words and phrases, as well as their relative position to the potential command boundaries. Decoding is done by examining the product of the weights for the features that are present. We also propose several enhancements to the approach, such as combining it with a more effective language model at the speech recognition stage to generate additional tokens for the identification model. We conducted several experiments to evaluate the proposed approach, and the results are described.
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The Predictive Power of Game Structure in Dialogue Act Recognition: Experimental Results Using Maximum Entropy Estimation
Authors:
Massimo Poesio, University of Edinburgh, HCRC (U.K.)
Andrei Mikheev, Harlequin (U.K.)
Page (NA) Paper number 606
Abstract:
Recognizing the dialogue act(s) performed by means of an utterance involves combining top-down expectations about the next likely `move' in a dialogue with bottom-up information extracted from the speech signal. We compared two ways of generating expectations: one which makes the expectations depend only on the previous act (as in a bigram model), and one which also takes into account the fact that individual dialogue acts play a role as part of larger conversational structures (`games'). Our models were built by training over the HCRC MapTask corpus using the LTG implementation of maximum entropy estimation. We achieved an accuracy of 38.6% using bigrams, of 50.6% taking game structure into account; adding information about speaker change resulted in an accuracy of 41.8% with bigrams, 54% with game structure. These results indicate that exploiting game structure does lead to improved expectations.
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A Schema Based Approach To Dialog Control
Authors:
Paul C. Constantinides, Carnegie Mellon University (USA)
Scott Hansma, Carnegie Mellon University (USA)
Chris Tchou, Carnegie Mellon University (USA)
Alexander I. Rudnicky, Carnegie Mellon University (USA)
Page (NA) Paper number 637
Abstract:
Frame-based approaches to spoken language interaction work well for limited tasks such as information access, given that the goal of the interaction is to construct a correct query then execute it. More complex tasks, however, can benefit from more active system participation. We describe two mechanisms that provide this, a modified stack that allows the system to track multiple topics, and form-specific schema that allow the system to deal with tasks that involve completion of multiple forms. Domain-dependent schema specify system behavior and are executed by a domain-independent engine. We describe implementations for a personal calendar system and for an air travel planning system.
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Expanding A Time-Sensitive Conversational Architecture For Turn-Taking To Handle Content-Driven Interruption
Authors:
Gregory Aist, Language Technologies Institute, Carnegie Mellon University (USA)
Page (NA) Paper number 928
Abstract:
Turn taking in spoken language systems has generally been push-to-talk or strict alternation (user speaks, system speaks, user speaks, ...) with some systems such as telephone-based systems handling barge-in (interruption by the user.) In this paper we describe our time sensitive conversational architecture for turn taking that not only allows alternating turns and barge in, but other conversational behaviors as well. This architecture allows backchanneling, prompting the user by taking more than one turn if necessary, and overlapping speech. The architecture is implemented in a Reading Tutor that listens to children read aloud, and helps them. We extended this architecture to allow the Reading Tutor to interrupt the student based on a non-self-corrected mistake - "content-driven interruption". To the best of our knowledge, the Reading Tutor is thus the first spoken language system to intentionally interrupt the user based on the content of the utterance.