Popularity Prediction for Social Media over Arbitrary Time Horizons
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
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).
Daniel Haimovich, Dima Karamshuk, Thomas Leeper, Evgeniy Riabenko, Milan Vojnovic
Liqi Yan, Qifan Wang, Yiming Cu, Fuli Feng, Xiaojun Quan, Xiangyu Zhang, Dongfang Liu
Patrick Lewis, Barlas Oğuz, Wenhan Xiong, Fabio Petroni, Wen-tau Yih, Sebastian Riedel