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Fast-LSTM acoustic model for distant speech recognition | IEEE Conference Publication | IEEE Xplore

Fast-LSTM acoustic model for distant speech recognition


Abstract:

The distant-talking automatic speech recognition (ASR) currently becomes an important task in a speech recognition area. Traditionally, hybrid Gaussian Mixture Model-Hidd...Show More

Abstract:

The distant-talking automatic speech recognition (ASR) currently becomes an important task in a speech recognition area. Traditionally, hybrid Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) approach are used for ASR. This paper will discuss some deep neural network (DNN) techniques for acoustic modeling, as well as lattice rescoring techniques for ASR. The proposed Fast-long short-term memory neural network (Fast-LSTM) acoustic model combines the time delay neural network (TDNN) and LSTM network to reduce the training time of the standard LSTM acoustic model.
Date of Conference: 12-14 January 2018
Date Added to IEEE Xplore: 29 March 2018
ISBN Information:
Electronic ISSN: 2158-4001
Conference Location: Las Vegas, NV, USA

References

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