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Neurally Optimized Decoder for Low Bitrate Speech Codec
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AI
DOI：10.1109/LSP.2021.3132557.png)
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摘要
En 中文
Recently, a conventional neural decoder for speech codec has shown promising performance. However, it typically requires some prior knowledge of decoding such as bit allocation or dequantization scheme, which is not a universal solution for many different kinds of speech codecs. In order to address this limitation, we propose a neurally optimized decoder based on a generative model which can directly reconstruct the speech from the bitstream without a prior knowledge. The proposed decoder mainly consists of two components: 1) a dequantization model to group and dequantize related bits from the bitstream and 2) a generative model to restore the speech conditioned on the output of the dequantization model. Through experiments with mixed excitation linear prediction (MELP), Advanced multi-band excitation (AMBE), and SPEEX at around 2.4 kb/s, it is showed that the proposed model showed better performance in most of the objective and subjective evaluation compared to the conventional speech codecs.
Keyword:
Decoding
Speech coding
Speech codecs
Bit rate
Encoding
Convolution
Knowledge engineering
Speech codecs
generative adversarial network
generative model
attention mechanism
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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