A MULTILINGUAL APPROACH TO TASK-ORIENTED MAN-MACHINE DIALOGUE BY VOICE

1988 ◽  
Vol 02 (03) ◽  
pp. 573-588
Author(s):  
PHILIPPE MORIN ◽  
JEAN-PAUL HATON ◽  
JEAN-MARIE PIERREL ◽  
GUENTHER RUSKE ◽  
WALTER WEIGEL
Keyword(s):  
Speech Recognition ◽  
Electronic Mail ◽  
Dialogue Systems ◽  
Human Speech ◽  
Artificial Languages ◽  
Man Machine Dialogue ◽  
Machine Communication ◽  
Task Oriented ◽  
Multilingual Approach ◽  
Multimedia Interfaces

In the framework of man-machine communication, oral dialogue has a particular place since human speech presents several advantages when used either alone or in multimedia interfaces. The last decade has witnessed a proliferation of research into speech recognition and understanding, but few systems have been defined with a view to managing and understanding an actual man-machine dialogue. The PARTNER system that we describe in this paper proposes a solution in the case of task oriented dialogue with the use of artificial languages. A description of the essential characteristics of dialogue systems is followed by a presentation of the architecture and the principles of the PARTNER system. Finally, we present the most recent results obtained in the oral management of electronic mail in French and German.

scholarly journals SPEECH / TEXT NOTE APP USING JAVASCRIPT AND HTML

2020 ◽  
pp. 129-134
Author(s):  
Suma S
Keyword(s):  
Speech Recognition ◽  
Language Processing ◽  
Recognition System ◽  
Research Field ◽  
Dialogue Systems ◽  
Human Communication ◽  
Personal Assistant ◽  
Recognition Systems ◽  
Machine Communication ◽  
Modern Era

Speech recognition is a vast research field for researchers in modern era. Earlier, the human language was processed by the computer system for speech recognition. Thus, the main objective is to develop recognition system which improves human to human communication by enabling human-machine communication by processing of text or speech. Various applications of speech recognition systems are present and these all includes various research challenges. Speech to text or text to speech is a part of Natural Language Processing which is a subfield of Artificial Intelligence. In Speech Recognition, spoken words/sentences are translated into text by computer. It is also known as Speech to Text (STT). Speech / Text note app could be very useful in number of applications. Especially in personal assistant bot, dictation, voice command based control system, audio transcriptions, quick notes with audio support, voice based authentication, etc. Web Speech API , It's a very powerful browser interface that allows you to record human speech and convert it into text. We will also use it to do the opposite - reading out strings in a human-like voice.Speech / Text note app can be classified into two main areas, dictation and human-computer dialogue systems.

scholarly journals Characterizing and Predicting Corrections in Spoken Dialogue Systems

2006 ◽  
Vol 32 (3) ◽  
pp. 417-438 ◽  
Author(s):  
Diane Litman ◽  
Julia Hirschberg ◽  
Marc Swerts
Keyword(s):  
Speech Recognition ◽  
Predictive Power ◽  
Classification Error ◽  
Dialogue Systems ◽  
Dialogue System ◽  
Spoken Dialogue Systems ◽  
Experimental Conditions ◽  
Spoken Dialogue ◽  
Spoken Dialogue System ◽  
Recognition Errors

This article focuses on the analysis and prediction of corrections, defined as turns where a user tries to correct a prior error made by a spoken dialogue system. We describe our labeling procedure of various corrections types and statistical analyses of their features in a corpus collected from a train information spoken dialogue system. We then present results of machine-learning experiments designed to identify user corrections of speech recognition errors. We investigate the predictive power of features automatically computable from the prosody of the turn, the speech recognition process, experimental conditions, and the dialogue history. Our best-performing features reduce classification error from baselines of 25.70–28.99% to 15.72%.

scholarly journals End-to-end optimization of goal-driven and visually grounded dialogue systems

2017 ◽  
Author(s):  
Florian Strub ◽  
Harm de Vries ◽  
Jérémie Mary ◽  
Bilal Piot ◽  
Aaron Courville ◽  
...  
Keyword(s):  
Gradient Algorithm ◽  
Planning Problem ◽  
Dialogue Systems ◽  
Generation Task ◽  
Policy Gradient ◽  
Specific Object ◽  
Complex Picture ◽  
History Of ◽  
End To End ◽  
Task Oriented

End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning. Yet, most current approaches cast human-machine dialogue management as a supervised learning problem, aiming at predicting the next utterance of a participant given the full history of the dialogue. This vision may fail to correctly render the planning problem inherent to dialogue as well as its contextual and grounded nature. In this paper, we introduce a Deep Reinforcement Learning method to optimize visually grounded task-oriented dialogues, based on the policy gradient algorithm. This approach is tested on the question generation task from the dataset GuessWhat?! containing 120k dialogues and provides encouraging results at solving both the problem of generating natural dialogues and the task of discovering a specific object in a complex picture.

scholarly journals An Evaluation of Chinese Human-Computer Dialogue Technology

2019 ◽  
Vol 1 (2) ◽  
pp. 187-200
Author(s):  
Zhengyu Zhao ◽  
Weinan Zhang ◽  
Wanxiang Che ◽  
Zhigang Chen ◽  
Yibo Zhang
Keyword(s):  
Artificial Intelligence ◽  
Large Scale ◽  
Data Sets ◽  
Dialogue Systems ◽  
Online Testing ◽  
User Intent ◽  
Existing Problems ◽  
Intelligent Processing ◽  
Task Oriented ◽  
Important Branch

The human-computer dialogue has recently attracted extensive attention from both academia and industry as an important branch in the field of artificial intelligence (AI). However, there are few studies on the evaluation of large-scale Chinese human-computer dialogue systems. In this paper, we introduce the Second Evaluation of Chinese Human-Computer Dialogue Technology, which focuses on the identification of a user's intents and intelligent processing of intent words. The Evaluation consists of user intent classification (Task 1) and online testing of task-oriented dialogues (Task 2), the data sets of which are provided by iFLYTEK Corporation. The evaluation tasks and data sets are introduced in detail, and meanwhile, the evaluation results and the existing problems in the evaluation are discussed.

scholarly journals Memory-Augmented Dialogue Management for Task-Oriented Dialogue Systems

2019 ◽  
Vol 37 (3) ◽  
pp. 1-30 ◽  
Author(s):  
Zheng Zhang ◽  
Minlie Huang ◽  
Zhongzhou Zhao ◽  
Feng Ji ◽  
Haiqing Chen ◽  
...  
Keyword(s):  
Dialogue Systems ◽  
Dialogue Management ◽  
Task Oriented
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