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This paper presents our work on developing acoustic models using deep neural networks (DNN) for low resource languages. This is considered one of the challenging problems in automatic speech recognition (ASR) as DNNs need large amount of data for building efficient models. The techniques explored in this approach use a common idea of transferring knowledge from models of high resource language to...
Acoustic model performance typically decreases when evaluated on a dialectal variation of the same language that was not used during training. Similarly, models simultaneously trained on a group of dialects tend to underperform dialect-specific models. In this paper, we report on our efforts towards building a unified acoustic model that can serve a multi-dialectal language. Two techniques are presented:...
This paper establishes a speaker-independent pronunciation recognition and assessment system with 673 words for mandarin Chinese under the background of a Chinese learning system framework. The recognition part is based on HTK using HMM (Hidden Markov Models) and improved in the aspect of acoustic model. Making use of the recognition results and the log-likelihood obtained from the Viterbi coding,...
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