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About Deep Speech - Scaling Up End-To-End Speech Recognition
This document summarizes research on Deep Speech, a state-of-the-art end-to-end speech recognition system using deep learning. Key points: - Deep Speech uses a recurrent neural network (RNN) trained directly on audio data to recognize speech, without complex preprocessing pipelines used by traditional systems. - It outperforms previous methods on the Switchboard Hub5'00 corpus, achieving a 16.0% error rate. - Deep Speech also handles noisy environments better than commercial speech recognition systems, achieving a 19.1% error rate on a noisy dataset where commercial systems achieved 30.5% error. - Key aspects of Deep Speech include large-scale RNN training across multiple GPU
Detailed Information
| Author: | ['Subhankar Chakraborty'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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