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Research on language modeling for speech recognition has increasingly focused on the application of neural networks. Two competing concepts have been developed: On the one hand, feedforward neural networks representing an n-gram approach, on the other hand recurrent neural networks that may learn context dependencies spanning more than a fixed number of predecessor words. To the best of our knowledge,...
The smoothing of n-gram models is a core technique in language modelling (LM). Modified Kneser-Ney (mKN) ranges among one of the best smoothing techniques. This technique discounts a fixed quantity from the observed counts in order to approximate the Turing-Good (TG) counts. Despite the TG counts optimise the leaving-one-out (L1O) criterion, the discounting parameters introduced in mKN do not. Moreover,...
Recognizing Broadcast Conversational (BC) speech data is a difficult task, which can be regarded as one of the major challenges beyond the recognition of Broadcast News (BN).
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