Paper Info
Title
Grapheme And Multilingual Posterior Features For Under-Resourced Speech Recognition: A Study On Scottish Gaelic
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-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 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.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6639087
2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Keywords
Field
DocType
Automatic speech recognition, Kullback-Leibler divergence based hidden Markov model, grapheme, phoneme, posterior feature, under-resourced speech recognition, Scottish Gaelic
Pronunciation,Scottish Gaelic,Conditional probability,Pattern recognition,Grapheme,Computer science,Cepstrum,Speech recognition,Lexicon,Natural language processing,Artificial intelligence,Hidden Markov model
Conference
ISSN
Citations 
PageRank 
1520-6149
4
0.46
References 
Authors
10
3
Name
Order
Citations
PageRank
Ramya Rasipuram1576.90
Peter Bell219222.97
Mathew Magimai-Doss351654.76