| Autor: |
Yun Wang, Gil Keren, Didi Zhang, Kaustubh Kalgaonkar, Ju Lin, Christian Fuegen |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
ICASSP |
| DOI: |
10.1109/icassp39728.2021.9413595 |
| Popis: |
Packet loss may affect a wide range of applications that use voice over IP (VoIP), e.g. video conferencing. In this paper, we investigate a time-domain convolutional recurrent network (CRN) for online packet loss concealment. The CRN comprises a convolutional encoder-decoder structure and long short-term memory (LSTM) layers, which have been shown to be suitable for real-time speech enhancement applications. Moreover, we propose lookahead and masked training to further improve the performance of the CRN framework. Experimental results show that the proposed system outperforms a baseline system using only LSTM layers in terms of two objective metrics – perceptual evaluation of speech quality (PESQ) and short-term objective intelligibility (STOI); it also reduces the word error rate (WER) more than the baseline when used as a frontend for speech recognition. The advantage of the proposed system is also verified in a subjective evaluation by the mean opinion score (MOS). |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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