A Speech Packet Loss Concealment Method Based on Priori Mel-Spectrum and Neural Vocoder
For the neural network-based speech Packet Loss Concealment(PLC),the input features are crucial factors that directly affect the final recovery performance.Additionally,the challenge of restoring high natural speech through PLC remains to be addressed.To effectively recover packet loss speech and improve its naturalness,this paper proposes a PLC method of speech signal based on the priori Mel-spectrum and neural vocoder.The proposed method adopts an asymmetric encoding and decoding network structure.At the encoding stage,this method utilizes two independent encoding networks to extract the latent time-frequency features from the waveform and Mel-spectrogram,respectively.At the decoding stage,the latent time-frequency features are jointly fed into a neural vocoder which is composed of several temporal adaptive denor-malization layer to restore the lost speech signals and enhance the naturalness.Simulation experiments demonstrate that the proposed method outperforms two existing packet loss concealment algorithms in terms of perceptual evaluation of speech quality and short-time objective intelligibility.
packet loss concealmentMel-spectrumneural vocodertemporal adaptive de-normalization layertime-frequency features
万方数据
维普
