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arXiv · 2207.03282

NESC: Robust Neural End-2-End Speech Coding with GANs

Nicola Pia·Kishan Gupta·Srikanth Korse·Markus Multrus·Guillaume Fuchs
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Abstract

Neural networks have proven to be a formidable tool to tackle the problem of speech coding at very low bit rates. However, the design of a neural coder that can be operated robustly under real-world conditions remains a major challenge. Therefore, we present Neural End-2-End Speech Codec (NESC) a robust, scalable end-to-end neural speech codec for high-quality wideband speech coding at 3 kbps. The encoder uses a new architecture configuration, which relies on our proposed Dual-PathConvRNN (DPCRNN) layer, while the decoder architecture is based on our previous work Streamwise-StyleMelGAN. Our subjective listening tests on clean and noisy speech show that NESC is particularly robust to unseen conditions and signal perturbations.

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Nicola Pia, Kishan Gupta, Srikanth Korse, Markus Multrus, Guillaume Fuchs. 2022-07-07. NESC: Robust Neural End-2-End Speech Coding with GANs. https://arxiv.org/abs/2207.03282

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