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Comparing a high and low-level deep neural network implementation for automatic speech recognition
| Content Provider | ACM Digital Library |
|---|---|
| Author | Ray, Jessica Thompson, Brian Shen, Wade |
| Abstract | The use of deep neural networks (DNNs) has improved performance in several fields including computer vision, natural language processing, and automatic speech recognition (ASR). The increased use of DNNs in recent years has been largely due to performance afforded by GPUs, as the computational cost of training large networks on a CPU is prohibitive. Many training algorithms are well-suited to the GPU; however, writing hand-optimized GPGPU code is a significant undertaking. More recently, high-level libraries have attempted to simplify GPGPU development by automatically performing tasks such as optimization and code generation. This work utilizes Theano, a high-level Python library, to implement a DNN for the purpose of phone recognition in ASR. Performance is compared against a low-level, hand-optimized C++/CUDA DNN implementation from Kaldi, a popular ASR toolkit. Results show that the DNN implementation in Theano has CPU and GPU runtimes on par with that of Kaldi, while requiring approximately 95% less lines of code. |
| Starting Page | 41 |
| Ending Page | 46 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479970209 |
| DOI | 10.1109/HPTCDL.2014.12 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-11-16 |
| Access Restriction | Subscribed |
| Subject Keyword | Theano Automatic optimization Dnn Kaldi Machine learning Gpu Cuda Python Automatic speech recognition |
| Content Type | Text |
| Resource Type | Article |
