Frame-synchronous and local confidence measures for on-the-fly automatic speech recognition

2008 ◽  
Author(s):  
Joseph Razik ◽  
Odile Mella ◽  
Dominique Fohr ◽  
Jean-Paul Haton
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Confidence Measures

FRAME-SYNCHRONOUS AND LOCAL CONFIDENCE MEASURES FOR AUTOMATIC SPEECH RECOGNITION

2011 ◽  
Vol 25 (02) ◽  
pp. 157-182 ◽  
Author(s):  
JOSEPH RAZIK ◽  
ODILE MELLA ◽  
DOMINIQUE FOHR ◽  
JEAN-PAUL HATON
Keyword(s):  
Speech Recognition ◽  
Likelihood Ratio ◽  
Automatic Speech Recognition ◽  
Posterior Probability ◽  
State Of The Art ◽  
Confidence Measure ◽  
Confidence Measures ◽  
Automatic Transcription ◽  
Recognition Systems ◽  
Large Vocabulary Speech Recognition

In this paper, we introduce two new confidence measures for large vocabulary speech recognition systems. The major feature of these measures is that they can be computed without waiting for the end of the audio stream. We proposed two kinds of confidence measures: frame-synchronous and local. The frame-synchronous ones can be computed as soon as a frame is processed by the recognition engine and are based on a likelihood ratio. The local measures estimate a local posterior probability in the vicinity of the word to analyze. We evaluated our confidence measures within the framework of the automatic transcription of French broadcast news with the EER criterion. Our local measures achieved results very close to the best state-of-the-art measure (EER of 23% compared to 22.0%). We then conducted a preliminary experiment to assess the contribution of our confidence measure in improving the comprehension of an automatic transcription for the hearing impaired. We introduced several modalities to highlight words of low confidence in this transcription. We showed that these modalities used with our local confidence measure improved the comprehension of automatic transcription.

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

Contextual Recovery of Out-of-Lattice Named Entities in Automatic Speech Recognition

2019 ◽  
Author(s):  
Jack Serrino ◽  
Leonid Velikovich ◽  
Petar Aleksic ◽  
Cyril Allauzen
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Named Entities
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