  Recently, neural networks have proven to be effective in performing speech
coding task at low bitrates. However, under-utilization of intra-frame
correlations and the error of quantizer specifically degrade the reconstructed
audio quality. To improve the coding quality, we present an end-to-end neural
speech codec, namely CBRC (Convolutional and Bidirectional Recurrent neural
Codec). An interleaved structure using 1D-CNN and Intra-BRNN is designed to
exploit the intra-frame correlations more efficiently. Furthermore, Group-wise
and Beam-search Residual Vector Quantizer (GB-RVQ) is used to reduce the
quantization noise. CBRC encodes audio every 20ms with no additional latency,
which is suitable for real-time communication. Experimental results demonstrate
the superiority of the proposed codec when comparing CBRC at 3kbps with Opus at
12kbps.
