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corpus. Using a bigram phoneme language model, phoneme recognition experiments are performed on a two hour independent test set using the Viterbi decoding which show a relative 33.3% improvement by our CD-DNN acoustic model. We then present a filler based Hybrid DNN-HMM Keyword Spotting KWS system which to our knowledge is
Provides an overview of a speech-to-text (STT) and keyword search (KWS) system architecture build primarily on the top of the Kaldi toolkit and expands on a few highlights. The system was developed as a part of the research efforts of the Radical team while participating in the IARPA Babel program. Our aim was to
-grain sparsification (CGS), introduces hardware-aware sparsity during the DNN training, which leads to efficient weight memory compression and significant computation reduction during classification without losing accuracy. We apply the proposed approach to DNN design for keyword detection and speech recognition. When the
Speech recognition and keyword detection are becoming increasingly popular applications for mobile systems. These applications have large memory and compute resource requirements, making their implementation on a mobile device quite challenging. In this paper, we design low cost neural network architectures for
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