scholarly journals EERA-ASR: An Energy-Efficient Reconfigurable Architecture for Automatic Speech Recognition With Hybrid DNN and Approximate Computing

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 52227-52237 ◽  
Author(s):  
Bo Liu ◽  
Hai Qin ◽  
Yu Gong ◽  
Wei Ge ◽  
Mengwen Xia ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Energy Efficient ◽  
Reconfigurable Architecture ◽  
Approximate Computing

scholarly journals E-ERA: An energy-efficient reconfigurable architecture for RNNs using dynamically adaptive approximate computing

2017 ◽  
Vol 14 (15) ◽  
pp. 20170637-20170637 ◽  
Author(s):  
Bo Liu ◽  
Wei Dong ◽  
Tingting Xu ◽  
Yu Gong ◽  
Wei Ge ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Reconfigurable Architecture ◽  
Approximate Computing

scholarly journals Approximate LSTM Computing for Energy-Efficient Speech Recognition

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2004
Author(s):  
Junseo Jo ◽  
Jaeha Kung ◽  
Youngjoo Lee
Keyword(s):  
Speech Recognition ◽  
Energy Efficient ◽  
Short Term Memory ◽  
Recognition System ◽  
Similarity Score ◽  
Cmos Process ◽  
Approximate Computing ◽  
Processing Elements ◽  
On Chip ◽  
Computing Method

This paper presents an approximate computing method of long short-term memory (LSTM) operations for energy-efficient end-to-end speech recognition. We newly introduce the concept of similarity score, which can measure how much the inputs of two adjacent LSTM cells are similar to each other. Then, we disable the highly-similar LSTM operations and directly transfer the prior results for reducing the computational costs of speech recognition. The pseudo-LSTM operation is additionally defined for providing the approximate computation with reduced processing resolution, which can further relax the processing overheads without degrading the accuracy. In order to verify the proposed idea, in addition, we design an approximate LSTM accelerator in 65 nm CMOS process. The proposed accelerator newly utilizes a number of approximate processing elements (PEs) to support the proposed skipped-LSTM and pseudo-LSTM operations without degrading the energy efficiency. Moreover, sparsity-aware scheduling is introduced by introducing the small-sized on-chip SRAM buffer. As a result, the proposed work provides an energy-efficient but still accurate speech recognition system, which consumes 2.19 times less energy than the baseline architecture.

Using Clinician Annotations to Improve Automatic Speech Recognition of Stuttered Speech

2016 ◽  
Author(s):  
Peter A. Heeman ◽  
Rebecca Lunsford ◽  
Andy McMillin ◽  
J. Scott Yaruss
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition

Multi-Scale Context Adaptation for Improving Child Automatic Speech Recognition in Child-Adult Spoken Interactions

2017 ◽  
Author(s):  
Manoj Kumar ◽  
Daniel Bone ◽  
Kelly McWilliams ◽  
Shanna Williams ◽  
Thomas D. Lyon ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Context Adaptation ◽  
Multi Scale

Phoneme-to-Grapheme Conversion Based Large-Scale Pre-Training for End-to-End Automatic Speech Recognition

2020 ◽  
Author(s):  
Ryo Masumura ◽  
Naoki Makishima ◽  
Mana Ihori ◽  
Akihiko Takashima ◽  
Tomohiro Tanaka ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Large Scale ◽  
End To End
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