Speaker adaptation of language and prosodic models for automatic dialog act segmentation of speech

Kolář, Jáchym; Liu, Yang; Shriberg, Elizabeth

doi:10.1016/j.specom.2009.10.005

Cited by 9 publications

(7 citation statements)

References 38 publications

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“…Several past approaches to this task have used lexical features, prosodic features, or a combination of such features, for example, [1,2,3,4,5]. Studies on sentence segmentation have been conducted in different domains, including broadcast news, conversational telephone speech, lectures, and meetings.…”

Section: Introductionmentioning

confidence: 99%

Genre effects on automatic sentence segmentation of speech: A comparison of broadcast news and broadcast conversations

Kolář

Liu

Shriberg

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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We investigate genre effects on the task of automatic sentence segmentation, focusing on two important domains -broadcast news (BN) and broadcast conversation (BC). We employ an HMM model based on textual and prosodic information and analyze differences in segmentation accuracy and feature usage between the two genres using both manual and automatic speech transcripts. Experiments are evaluated using Czech broadcast corpora annotated for sentencelike units (SUs). Prosodic features capture information about pause, duration, pitch, and energy patterns. Textual knowledge sources include words, part-of-speech, and automatically induced classes. We also analyze effects of using additional textual data that is not annotated for SUs. Feature analysis reveals significant differences in both textual and prosodic feature usage patterns between the two genres. The analysis is important for building automatic understanding systems when limited matched-genre data are available, or for designing eventual genre-independent systems.

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Section: Introductionmentioning

confidence: 99%

Genre effects on automatic sentence segmentation of speech: A comparison of broadcast news and broadcast conversations

Kolář

Liu

Shriberg

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…For the pitch and RMS values, we obtain the max, min, mean, variance and the co-efficients of a second degree polynomial. Pause durations at word boundaries provide an additional useful feature (Kolář et al, 2006;Zimmermann, 2009). All numeric features are discretized into bins.…”

Section: Featuresmentioning

confidence: 99%

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Katagiri

Nakano

Fernández

et al. 2016

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We extend special thanks to our Local co-Chairs, Ron Artstein and Alesia Gainer, and their team of student volunteers. We know SIGDIAL 2016 would not have been possible without Ron and Alesia, who invested so much effort in arranging the conference venue and accommodations, handling registration, making banquet arrangements, and handling numerous other preparations for the conference. The student volunteers for on-site assistance also deserve our appreciation.Ethan Selfridge, Sponsorships Chair, has earned our appreciation for recruiting and liaising with our conference sponsors, many of whom continue to contribute year after year. Sponsorships support valuable aspects of the program, such as the invited speakers and conference banquet. In recognition of this, we gratefully acknowledge the support of our sponsors: (Platinum level) Microsoft Research, Xerox and PARC, Intel, (Gold level) Facebook, (Silver level) Amazon Alexa, Interactions, Educational Testing Service, Honda Research Institute, and Yahoo!. At the same time, we thank Priscilla Rasmussen at the ACL for tirelessly handling the financial aspects of sponsorship for SIGDIAL 2016, and for securing our ISBN.iii We also thank the SIGdial board, especially officers Amanda Stent, Jason Williams and Kristiina Jokinen for their advice and support from beginning to end.Finally, we thank all the authors of the papers in this volume, and all the conference participants for making this stimulating event a valuable opportunity for growth in the research areas of discourse and dialogue. AbstractThis paper presents an end-to-end framework for task-oriented dialog systems using a variant of Deep Recurrent QNetworks (DRQN). The model is able to interface with a relational database and jointly learn policies for both language understanding and dialog strategy. Moreover, we propose a hybrid algorithm that combines the strength of reinforcement learning and supervised learning to achieve faster learning speed. We evaluated the proposed model on a 20 Question Game conversational game simulator. Results show that the proposed method outperforms the modular-based baseline and learns a distributed representation of the latent dialog state. IntroductionTask-oriented dialog systems have been an important branch of spoken dialog system (SDS) research (Raux et al., 2005; Young, 2006; Bohus and Rudnicky, 2003). The SDS agent has to achieve some predefined targets (e.g. booking a flight) through natural language interaction with the users. The typical structure of a task-oriented dialog system is outlined in Figure 1 (Young, 2006). This pipeline consists of several independently-developed modules: natural language understanding (the NLU) maps the user utterances to some semantic representation. This information is further processed by the dialog state tracker (DST), which accumulates the input of the turn along with the dialog history. The DST outputs the current dialog state and the dialog policy selects the next system action based on the dialog state. Then natural language gene...

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“…Kolár et al (2010) used dialogue act segmentation and dialogue act classification using simple lexical and prosodic knowledge sources in automatic meeting applications. Zhang et al (2006) expressed group actions as a two layer process by hidden Markov model (HMM) framework.…”

Section: Related Workmentioning

confidence: 99%

A new framework for mining frequent interaction patterns from meeting databases

Fariha

Ahmed

Leung

et al. 2015

Engineering Applications of Artificial Intelligence

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Speaker adaptation of language and prosodic models for automatic dialog act segmentation of speech

Cited by 9 publications

References 38 publications

Genre effects on automatic sentence segmentation of speech: A comparison of broadcast news and broadcast conversations

Genre effects on automatic sentence segmentation of speech: A comparison of broadcast news and broadcast conversations

Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

A new framework for mining frequent interaction patterns from meeting databases

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