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Abstract
In this paper we investigate the use of Multi-level adaptive networks (MLAN) to incorporate out-of-domain data when training large vocabulary speech recognition systems. In a set of experiments on multi-genre broadcast data and on TED lecture recordings we present results using of out-of-domain features in a hybrid DNN system and explore tandem systems using a variety of input acoustic features. Our experiments indicate using the MLAN approach in both hybrid and tandem systems results in consistent reductions in word error rate of 5-10% relative.
| Original language | English |
|---|---|
| Title of host publication | Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Pages | 6975-6979 |
| Number of pages | 5 |
| ISBN (Print) | 978-1-4799-0356-6 |
| DOIs | |
| Publication status | Published - 2013 |
Keywords
- error analysis
- speech recognition
- vocabulary
- TED lecture recordings
- hybrid ASR systems
- multigenre broadcast data
- multilevel adaptive networks
- out-of-domain data
- vocabulary speech recognition systems
- word error rate
- Acoustics
- Adaptation models
- Hidden Markov models
- Neural networks
- Speech
- Speech recognition
- Training
- BBC
- MLAN
- TED
- deep neural networks
- hybrid
- tandem
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