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In this paper, we describe the THUEE (Department of Electronic Engineering, Tsinghua University) team's method of building language models (LMs) for the OpenKWS 2015 Evaluation held by the National Institute of Standards and Technology (NIST). Due to the very limited in-domain data provided by NIST, it takes most of our time and efforts to make good use of the out-of-domain data. There are three main...
The Context-Dependent Deep-Neural-Network HMM, or CD-DNN-HMM, is a powerful acoustic modeling technique. Its training process typically involves unsupervised pre-training and supervised fine-tuning. In the paper, we demonstrate that the performance of DNNs can be improved by utilizing a large amount of unlabeled data in the training procedure. In our method, CD-DNN-HMM trained using 309 hours of unlabeled...
Automatic multilingual speech recognition is always a difficult task. This paper presents recent work on the development of a Mandarin-English bilingual speech recognition system. A unified single set of bilingual acoustic models based on a novel State-Time-Alignment (STA) method is proposed to balance the performance and the complexity of the bilingual speech recognition system, and a comparison...
In this paper, we describe two approaches for language identification (LID) using support vector machines (SVM) and phonetic n-gram. One is to use the language model scores of phone sequences to do SVM training. The other is to use the n-gram probabilities of those phones to train SVM models. For the second approach, we propose a new effective normalization method. In the experiments of 30 s test...
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