| Autor: |
Laptev, Aleksandr, Korostik, Roman, Svischev, Aleksey, Andrusenko, Andrei, Medennikov, Ivan, Rybin, Sergey |
| Rok vydání: |
2020 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/CISP-BMEI51763.2020.9263564 |
| Popis: |
Data augmentation is one of the most effective ways to make end-to-end automatic speech recognition (ASR) perform close to the conventional hybrid approach, especially when dealing with low-resource tasks. Using recent advances in speech synthesis (text-to-speech, or TTS), we build our TTS system on an ASR training database and then extend the data with synthesized speech to train a recognition model. We argue that, when the training data amount is relatively low, this approach can allow an end-to-end model to reach hybrid systems' quality. For an artificial low-to-medium-resource setup, we compare the proposed augmentation with the semi-supervised learning technique. We also investigate the influence of vocoder usage on final ASR performance by comparing Griffin-Lim algorithm with our modified LPCNet. When applied with an external language model, our approach outperforms a semi-supervised setup for LibriSpeech test-clean and only 33% worse than a comparable supervised setup. Our system establishes a competitive result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for test-other. |
| Databáze: |
arXiv |
| Externí odkaz: |
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