Detailed pronunciation variant modeling for speech transcription

2010 ◽  
Author(s):  
Denis Jouvet ◽  
Dominique Fohr ◽  
Irina Illina
Keyword(s):  
Speech Transcription

The Vicomtech-PRHLT Speech Transcription Systems for the IberSPEECH-RTVE 2018 Speech to Text Transcription Challenge

2018 ◽  
Author(s):  
Haritz Arzelus ◽  
Aitor Alvarez ◽  
Conrad Bernath ◽  
Eneritz García ◽  
Emilio Granell ◽  
...  
Keyword(s):  
Speech Transcription

On Employing a Highly Mismatched Crowd for Speech Transcription

2016 ◽  
Author(s):  
Purushotam Radadia ◽  
Rahul Kumar ◽  
Kanika Kalra ◽  
Shirish Karande ◽  
Sachin Lodha
Keyword(s):  
Speech Transcription

scholarly journals Lithuanian Broadcast Speech Transcription Using Semi-supervised Acoustic Model Training

2016 ◽  
Vol 81 ◽  
pp. 107-113 ◽  
Author(s):  
Rasa Lileikytė ◽  
Arseniy Gorin ◽  
Lori Lamel ◽  
Jean-Luc Gauvain ◽  
Thiago Fraga-Silva
Keyword(s):  
Acoustic Model ◽  
Model Training ◽  
Speech Transcription

Advances in speech transcription at IBM under the DARPA EARS program

2006 ◽  
Vol 14 (5) ◽  
pp. 1596-1608 ◽  
Author(s):  
S.F. Chen ◽  
B. Kingsbury ◽  
Lidia Mangu ◽  
D. Povey ◽  
G. Saon ◽  
...  
Keyword(s):  
Speech Transcription

scholarly journals TEDxSK and JumpSK: A New Slovak Speech Recognition Dedicated Corpus

2017 ◽  
Vol 68 (2) ◽  
pp. 346-354
Author(s):  
Ján Staš ◽  
Daniel Hládek ◽  
Peter Viszlay ◽  
Tomáš Koctúr
Keyword(s):  
Speech Recognition ◽  
Total Duration ◽  
Principal Component ◽  
Speech Segmentation ◽  
Word Error Rate ◽  
Speech Database ◽  
Recognition Systems ◽  
Speech Transcription ◽  
Speech Segments

Abstract This paper describes a new Slovak speech recognition dedicated corpus built from TEDx talks and Jump Slovakia lectures. The proposed speech database consists of 220 talks and lectures in total duration of about 58 hours. Annotated speech database was generated automatically in an unsupervised manner by using acoustic speech segmentation based on principal component analysis and automatic speech transcription using two complementary speech recognition systems. The evaluation data consisting of 50 manually annotated talks and lectures in total duration of about 12 hours, has been created for evaluation of the quality of Slovak speech recognition. By unsupervised automatic annotation of TEDx talks and Jump Slovakia lectures we have obtained 21.26% of new speech segments with approximately 9.44% word error rate, suitable for retraining or adaptation of acoustic models trained beforehand.

Efficient speech transcription through respeaking

2013 ◽  
Author(s):  
Matthias Sperber ◽  
Graham Neubig ◽  
Christian Fügen ◽  
Satoshi Nakamura ◽  
Alex Waibel
Keyword(s):  
Speech Transcription
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