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Recursive word topology structure is commonly found in natural language sentences, and discovering this structure can help us to not only identify the units that a sentence contains but also how they interact to form a whole. In this paper, we explore a novel recursive neural network (RNN) based word topology model (WordTM) for hierarchical phrase-based (HPB) speech translation, which captures the...
In this paper, we propose an unsupervised phrase-based data selection model, address the problem of selecting no-domain-specific language model (LM) training data to build adapted LM for use. In spoken language translation (SLT) system, we aim at finding the LM training sentences which are similar to the translation task. Compared with the traditional bag-of-words models, the phrase-based data selection...
Hierarchical phrase-based (HPB) translation has been introduced to speech-to-speech (S2S) translation system on mobile terminals, such as smartphones. However, it suffers from the explosive growth in the number of rules along with the increment in decoding time for S2S translation system when the memory and decoding speed is restricted. In this paper, we propose a nesting HPB model to capture the...
In this paper, we first review several approaches of feature extraction algorithms in robust speech recognition, e.g. Mel frequency cepstral coefficients (MFCC) [1], perceptual linear prediction (PLP) [2] and power-normalized cepstral coefficients (PNCC) [3]. A new feature extraction algorithm for noise robust speech recognition is proposed, in which medium-time processing works as noise suppression...
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