Tree-based context clustering using speech recognition features for acoustic model training of speech synthesis

2015 ◽  
Author(s):  
Supadaech Chanjaradwichai ◽  
Atiwong Suchato ◽  
Proadpran Punyabukkana
Keyword(s):  
Speech Recognition ◽  
Speech Synthesis ◽  
Acoustic Model ◽  
Model Training

Pipelining Acoustic Model Training for Speech Recognition Using Storm

2013 ◽  
Author(s):  
Dinkar Sitaram ◽  
Haripriya Srinivasaraghavan ◽  
Kapish Agarwal ◽  
Amritanshu Agrawal ◽  
Neha Joshi ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Acoustic Model ◽  
Model Training

scholarly journals Acoustic Model Training, using Kaldi, for Automatic Whispery Speech Recognition

2018 ◽  
Author(s):  
Piotr Kozierski ◽  
Talar Sadalla ◽  
Szymon Drgas ◽  
Adam Dąbrowski ◽  
Joanna Ziętkiewicz ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Acoustic Model ◽  
Model Training

scholarly journals Using Privacy-Transformed Speech in the Automatic Speech Recognition Acoustic Model Training

2020 ◽  
Author(s):  
Askars Salimbajevs
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
State Of The Art ◽  
Speaker Verification ◽  
Voice Conversion ◽  
Acoustic Model ◽  
Acoustic Models ◽  
Speech Data ◽  
Model Training ◽  
The Voice

Automatic Speech Recognition (ASR) requires huge amounts of real user speech data to reach state-of-the-art performance. However, speech data conveys sensitive speaker attributes like identity that can be inferred and exploited for malicious purposes. Therefore, there is an interest in the collection of anonymized speech data that is processed by some voice conversion method. In this paper, we evaluate one of the voice conversion methods on Latvian speech data and also investigate if privacy-transformed data can be used to improve ASR acoustic models. Results show the effectiveness of voice conversion against state-of-the-art speaker verification models on Latvian speech and the effectiveness of using privacy-transformed data in ASR training.

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