A Survey on Dialogue Systems

Chen, Hongshen; Liu, Xiaorui; Yin, Dawei; Tang, Jiliang

doi:10.1145/3166054.3166058

Cited by 413 publications

(81 citation statements)

References 62 publications

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“…As a traditional paradigm shift, recent works in this area have addressed a series of data-driven, end-to-end trainable, non-goal-driven systems based on generative probabilistic models [131]. As such, these models can be viewed as artificial cognitive systems, aimed at grouping and carrying out traditional dialogue management tasks: language understanding, reasoning, decision-making, and natural language generation.…”

Section: Progress In Speech Recognition and Synthesis As Well As mentioning

confidence: 99%

Speech Technology Progress Based on New Machine Learning Paradigm

Delić

Perić

Secujski

et al. 2019

Computational Intelligence and Neuroscience

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Speech technologies have been developed for decades as a typical signal processing area, while the last decade has brought a huge progress based on new machine learning paradigms. Owing not only to their intrinsic complexity but also to their relation with cognitive sciences, speech technologies are now viewed as a prime example of interdisciplinary knowledge area. This review article on speech signal analysis and processing, corresponding machine learning algorithms, and applied computational intelligence aims to give an insight into several fields, covering speech production and auditory perception, cognitive aspects of speech communication and language understanding, both speech recognition and text-to-speech synthesis in more details, and consequently the main directions in development of spoken dialogue systems. Additionally, the article discusses the concepts and recent advances in speech signal compression, coding, and transmission, including cognitive speech coding. To conclude, the main intention of this article is to highlight recent achievements and challenges based on new machine learning paradigms that, over the last decade, had an immense impact in the field of speech signal processing.

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Section: Progress In Speech Recognition and Synthesis As Well As mentioning

confidence: 99%

Speech Technology Progress Based on New Machine Learning Paradigm

Delić

Perić

Secujski

et al. 2019

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The typology dimensions further seems to provide a novel take on chatbot classification as compared to earlier attempts, such as the distinction of four kinds of interactional styles in conversational systems discussed by the IBM's research group on conversational UX design, and the proposed dichotomy of Chen et al [3] between task oriented and non-task oriented conversational systems. Regarding the former classification, the interaction styles presented may to some degree be seen as a consequence of chatbot type.…”

Section: Discussionmentioning

confidence: 99%

Different Chatbots for Different Purposes: Towards a Typology of Chatbots to Understand Interaction Design

Følstad

Skjuve

Brandtzæg

2019

Lecture Notes in Computer Science

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Chatbots are emerging as interactive systems. However, we lack knowledge on how to classify chatbots and how such classification can be brought to bear in analysis of chatbot interaction design. In this workshop paper, we propose a typology of chatbots to support such classification and analysis. The typology dimensions address key characteristics that differentiate current chatbots: the duration of the user's relation with the chatbot (short-term and long-term), and the locus of control for user's interaction with the chatbot (userdriven and chatbot-driven). To explore the usefulness of the typology, we present four example chatbot purposes for which the typology may support analysis of high-level chatbot interaction design. Furthermore, we analyse a sample of 57 chatbots according to the typology dimensions. The relevance and application of the typology for developers and service providers are discussed.

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“…There is no doubt that the existing end-to-end models are still far from perfect [77]. Despite many achievements, there are still some challenging problems.…”

Section: Discussionmentioning

confidence: 99%

A Review of Deep Learning Based Speech Synthesis

Ning

et al. 2019

Applied Sciences

107

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Speech synthesis, also known as text-to-speech (TTS), has attracted increasingly more attention. Recent advances on speech synthesis are overwhelmingly contributed by deep learning or even end-to-end techniques which have been utilized to enhance a wide range of application scenarios such as intelligent speech interaction, chatbot or conversational artificial intelligence (AI). For speech synthesis, deep learning based techniques can leverage a large scale of <text, speech> pairs to learn effective feature representations to bridge the gap between text and speech, thus better characterizing the properties of events. To better understand the research dynamics in the speech synthesis field, this paper firstly introduces the traditional speech synthesis methods and highlights the importance of the acoustic modeling from the composition of the statistical parametric speech synthesis (SPSS) system. It then gives an overview of the advances on deep learning based speech synthesis, including the end-to-end approaches which have achieved start-of-the-art performance in recent years. Finally, it discusses the problems of the deep learning methods for speech synthesis, and also points out some appealing research directions that can bring the speech synthesis research into a new frontier.

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A Survey on Dialogue Systems

Cited by 413 publications

References 62 publications

Speech Technology Progress Based on New Machine Learning Paradigm

Speech Technology Progress Based on New Machine Learning Paradigm

Different Chatbots for Different Purposes: Towards a Typology of Chatbots to Understand Interaction Design

A Review of Deep Learning Based Speech Synthesis

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