Corpus ID: 14290584

Kullback-Leibler Divergence-Based ASR Training Data Selection

  title={Kullback-Leibler Divergence-Based ASR Training Data Selection},
  author={E. Gouv{\^e}a and Marelie Hattingh Davel},
European Media Laboratory GmbH, Heidelberg, Germany Multilingual Speech Technologies, North-West University, Vanderbijlpark, South Africa 
Automatic speech recognition for resource–scarce environments
Thesis (PhD (Computer and Electronic Engineering))--North-West University, Potchefstroom Campus, 2013.
Efficient data selection for ASR
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A submodular optimization approach to sentence set selection
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  • Computer Science
  • 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2014
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It is shown that the proposed subset selection scheme leads to performance improvements over state of the art speech recognition systems in terms of both speech recognition word error rate (WER) and language model perplexity (PPL). Expand
Methods for optimal text selection
This work addresses how one can take advantage of control over the content of the speech data base, by discussing a number of variants of “greedy” text selection methods and showing their application in a variety of examples. Expand
The Design for the Wall Street Journal-based CSR Corpus
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Data selection for speech recognition
In contrast to the common belief that "there is no data like more data", it is found possible to select a highly informative subset of data that produces recognition performance comparable to a system that makes use of a much larger amount of data. Expand
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A system for quickly and cheaply building transcribed speech corpora containing utterances from many speakers in a variety of acoustic conditions, used to collect over 3000 hours of transcribed audio in 17 languages around the world. Expand
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Pattern Recognition and Machine Learning
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Pattern Recognition and Machine Learning (Information Science and Statistics)
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