Name
Abstract | ||
|---|---|---|
We propose an approach for acoustic modeling of Hindi speech by borrowing from English data, for the purpose of Hindi LVCSR. Hindi, like many Indian languages, has a significant speaker base but there have not been a lot of resources to obtain large amounts of transcribed Hindi data for LVCSR. We compare a baseline Gaussian model-sharing approach with DNN training. A widely used data-borrowing method with DNN is to firstly train a DNN with English, for which a large amount of training data is available; then the whole DNN, except the last layer, is fine-tuned by using the target Hindi data. We propose to do phonetic mapping between Hindi and English in the first stage, training Hindi acoustic models by sharing data between Hindi-English phone pairs in the second stage, and finally fine-tuning the acoustic model by using the Hindi data. We evaluate and compare these approaches with experiments using 1 hour of transcribed Hindi data and 15 hours of Wall Street Journal English data. Experiments show that the proposed method significantly outperforms conventional baseline models in a low-resource setting for phone recognition tasks. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICALIP.2014.7009923 | Audio, Language and Image Processing |
Keywords | DocType | Citations |
gaussian processes,acoustic signal processing,feedforward neural nets,hidden markov models,learning (artificial intelligence),natural language processing,speaker recognition,speech processing,dnn training,gmm,gaussian mixture models,hmm,hindi lvcsr,hindi speech recognition,hindi-english phone pairs,indian languages,wall street journal english data,acoustic modeling,baseline gaussian model-sharing approach,data sharing,deep neural network,feed-forward network,low-resource settings,phone recognition tasks,phonetic mapping,hindi lvscr,data borrowing,low resource,phone mapping,feature extraction,speech,speech recognition,data models,acoustics | Conference | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anik Dey | 1 | 14 | 3.23 |
| Weibin Zhang | 2 | 31 | 10.03 |
| Pascale Fung | 3 | 653 | 135.24 |