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 [BibTeX] [Marc21]
Grapheme and Multilingual Posterior Features For Under-Resource Speech Recognition: A Study on Scottish Gaelic
Type of publication: Idiap-RR
Citation: Rasipuram_Idiap-RR-34-2012
Number: Idiap-RR-34-2012
Year: 2012
Month: 12
Institution: Idiap
Abstract: Standard automatic speech recognition (ASR) systems use phonemes as subword units. Thus, one of the primary resource required to build a good ASR system is a well developed phoneme pronunciation lexicon. However, under-resourced languages typically lack such lexical resources. In this paper, we investigate recently proposed grapheme-based ASR in the framework of Kullback-Leibler divergence based hidden Markov model (KL-HMM) for under-resource languages, particularly Scottish Gaelic which has no lexical resources. More specifically, we study the use of grapheme and multilingual phoneme class conditional probabilities (posterior features) as feature observations in KL-HMM. ASR studies conducted show that the proposed approach yields better system when compared to conventional HMM/GMM approach using cepstral features. Furthermore, grapheme posterior features estimated using both auxiliary data and Gaelic data yield the best system.
Keywords: Automatic Speech Recognition, grapheme, Kullback-Leibler divergence based hidden Markov model, phoneme, Posterior features, Scottish Gaelic, under-resource speech recognition
Projects Idiap
Authors Rasipuram, Ramya
Bell, Peter
Magimai.-Doss, Mathew
Added by: [ADM]
Total mark: 0
  • Rasipuram_Idiap-RR-34-2012.pdf (MD5: ac36a59a10d5e2b6726b6e3214ac932d)