Grapheme and multilingual posterior features for under-resourced speech recognition: a study on Scottish Gaelic

Standard automatic speech recognition (ASR) systems use phonemes as subword units. Thus, one of the primary resources 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-resourced 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 the KL-HMM. ASR studies conducted show that the proposed approach yields better system compared to the conventional HMM/GMM approach using cepstral features. Furthermore, grapheme posterior features estimated using both auxiliary data and Gaelic data yield the best system.