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About Improving English Conversational Telephone Speech Recognition
This document describes research into improving English conversational telephone speech recognition. It investigates techniques like speaker-dependent bottleneck features, deep bidirectional long short-term memory acoustic models, data augmentation, and score fusion of deep neural network and BLSTM models. The resulting system achieves a competitive 7.8% word error rate on the Switchboard portion of the HUB5 2000 evaluation set, which is the state-of-the-art at the time. Recurrent neural network language models are also used for hypothesis rescoring.
Detailed Information
| Author: | ['Singitan Yomiyu'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 6 |
| Language: | English |
| Format: | |
| Price: | FREE |
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