`

Timezone: »

 
Poster
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices
Jinhwan Park · Yoonho Boo · Iksoo Choi · Sungho Shin · Wonyong Sung

Tue Dec 04 02:00 PM -- 04:00 PM (PST) @ Room 210 #88

Real-time automatic speech recognition (ASR) on mobile and embedded devices has been of great interests for many years. We present real-time speech recognition on smartphones or embedded systems by employing recurrent neural network (RNN) based acoustic models, RNN based language models, and beam-search decoding. The acoustic model is end-to-end trained with connectionist temporal classification (CTC) loss. The RNN implementation on embedded devices can suffer from excessive DRAM accesses because the parameter size of a neural network usually exceeds that of the cache memory and the parameters are used only once for each time step. To remedy this problem, we employ a multi-time step parallelization approach that computes multiple output samples at a time with the parameters fetched from the DRAM. Since the number of DRAM accesses can be reduced in proportion to the number of parallelization steps, we can achieve a high processing speed. However, conventional RNNs, such as long short-term memory (LSTM) or gated recurrent unit (GRU), do not permit multi-time step parallelization. We construct an acoustic model by combining simple recurrent units (SRUs) and depth-wise 1-dimensional convolution layers for multi-time step parallelization. Both the character and word piece models are developed for acoustic modeling, and the corresponding RNN based language models are used for beam search decoding. We achieve a competitive WER for WSJ corpus using the entire model size of around 15MB and achieve real-time speed using only a single core ARM without GPU or special hardware.

Author Information

Jinhwan Park (Seoul National University)
Yoonho Boo (Seoul National University)
Iksoo Choi (Seoul National University)
Sungho Shin (Seoul National University)
Wonyong Sung (Seoul National University)

More from the Same Authors