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Wavenet based low rate speech coding

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arxiv 1712.01120 v1 pith:V2PH66AI submitted 2017-12-01 eess.AS cs.SDeess.SP

classification eess.AScs.SDeess.SP
keywords speechcodermodelgenerativeparametricrateusedwavenet
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Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used. We describe how a WaveNet generative speech model can be used to generate high quality speech from the bit stream of a standard parametric coder operating at 2.4 kb/s. We compare this parametric coder with a waveform coder based on the same generative model and show that approximating the signal waveform incurs a large rate penalty. Our experiments confirm the high performance of the WaveNet based coder and show that the speech produced by the system is able to additionally perform implicit bandwidth extension and does not significantly impair recognition of the original speaker for the human listener, even when that speaker has not been used during the training of the generative model.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Salient Speech Representations Based on Cloned Networks

    eess.AS 2019-08 conditional novelty 6.0 of 10

    Clone-based training with shared-weight encoders extracts robust 12-dimensional speech features, and these features outperform PCA when used as WaveNet conditioning for coding and enhancement.

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