%Aigaion2 BibTeX export from Idiap Publications
%Saturday 21 December 2024 05:37:47 PM

@TECHREPORT{Rasipuram_Idiap-RR-34-2012,
         author = {Rasipuram, Ramya and Bell, Peter and Magimai.-Doss, Mathew},
       keywords = {Automatic Speech Recognition, grapheme, Kullback-Leibler divergence based hidden Markov model, phoneme, Posterior features, Scottish Gaelic, under-resource speech recognition},
       projects = {Idiap},
          month = {12},
          title = {Grapheme and Multilingual Posterior Features For Under-Resource Speech Recognition: A Study on Scottish Gaelic},
           type = {Idiap-RR},
         number = {Idiap-RR-34-2012},
           year = {2012},
    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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2012/Rasipuram_Idiap-RR-34-2012.pdf}
}