Session U. Speech Communication: Automatic Speech Recognition, Speech Understanding, and Speech Science

1974 ◽  
Vol 56 (S1) ◽  
pp. S43-S43
Author(s):  
Donald E. Walker
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Speech Communication ◽  
Speech Understanding ◽  
Speech Science

Automatic Prosody Labelling and Assessment

2020 ◽  
pp. 645-656
Author(s):  
Andrew Rosenberg ◽  
Mark Hasegawa-Johnson
Keyword(s):  
Speech Recognition ◽  
Best Practices ◽  
Automatic Speech Recognition ◽  
State Of The Art ◽  
Linguistic Analysis ◽  
The State ◽  
Speech Understanding ◽  
Front End ◽  
Open Questions

Automatic prosody labelling is a useful front-end for automatic speech recognition, for automatic speech understanding, and for the development of corpora used to create speech synthesizers. Automatic labelling of prosody has also proven to be quite useful in the linguistic analysis of new speaking styles in a known language. This chapter provides a survey of the state-of-the-art best practices and open questions in the automatic labelling of prosodic information and its assessment. It describes the major prosodic inventories that are used in prosody labelling. It then discusses the relevance of acoustics and syntax in automatic labelling. A brief description of AuToBI, a tool that performs automatic ToBI labelling of US English, is provided. The chapter concludes by discussing methods of evaluating automatic prosody labelling.

scholarly journals Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems

2021 ◽  
pp. 1-12
Author(s):  
Manaal Faruqui ◽  
Dilek Hakkani-Tür
Keyword(s):  
Speech Recognition ◽  
Natural Language ◽  
Automatic Speech Recognition ◽  
Daily Life ◽  
Natural Language Understanding ◽  
Speech Understanding ◽  
Language Understanding ◽  
Dialog Systems ◽  
Research Areas ◽  
The World

Abstract As more users across the world are interacting with dialog agents in their daily life, there is a need for better speech understanding that calls for renewed attention to the dynamics between research in automatic speech recognition (ASR) and natural language understanding (NLU). We briefly review these research areas and lay out the current relationship between them. In light of the observations we make in this paper, we argue that (1) NLU should be cognizant of the presence of ASR models being used upstream in a dialog system’s pipeline, (2) ASR should be able to learn from errors found in NLU, (3) there is a need for end-to-end datasets that provide semantic annotations on spoken input, (4) there should be stronger collaboration between ASR and NLU research communities.

Using Clinician Annotations to Improve Automatic Speech Recognition of Stuttered Speech

2016 ◽  
Author(s):  
Peter A. Heeman ◽  
Rebecca Lunsford ◽  
Andy McMillin ◽  
J. Scott Yaruss
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition

Multi-Scale Context Adaptation for Improving Child Automatic Speech Recognition in Child-Adult Spoken Interactions

2017 ◽  
Author(s):  
Manoj Kumar ◽  
Daniel Bone ◽  
Kelly McWilliams ◽  
Shanna Williams ◽  
Thomas D. Lyon ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Context Adaptation ◽  
Multi Scale

Phoneme-to-Grapheme Conversion Based Large-Scale Pre-Training for End-to-End Automatic Speech Recognition

2020 ◽  
Author(s):  
Ryo Masumura ◽  
Naoki Makishima ◽  
Mana Ihori ◽  
Akihiko Takashima ◽  
Tomohiro Tanaka ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Large Scale ◽  
End To End
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