REPORT Rasipuram_Idiap-RR-34-2012/IDIAP Grapheme and Multilingual Posterior Features For Under-Resource Speech Recognition: A Study on Scottish Gaelic Rasipuram, Ramya Bell, Peter Magimai-Doss, Mathew Automatic Speech Recognition grapheme Kullback-Leibler divergence based hidden Markov model phoneme Posterior features Scottish Gaelic under-resource speech recognition EXTERNAL http://publications.idiap.ch/attachments/reports/2012/Rasipuram_Idiap-RR-34-2012.pdf PUBLIC Idiap-RR-34-2012 2012 Idiap December 2012 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.