16th Annual Conference of the International Speech Communication Association

Dresden, Germany
September 6-10, 2015

On Compressibility of Neural Network Phonological Features for Low Bit Rate Speech Coding

Afsaneh Asaei, Milos Cernak, Hervé Bourlard

Idiap Research Institute, Switzerland

Phonological features extracted by neural network have shown interesting potential for low bit rate speech vocoding. The time span of phonological features is wider than that of the phonetic features, and thus fewer frames need to be transmitted. Moreover, the binary nature of phonological features enables a higher compression ratio at minor quality cost.
    In this paper, we study the compressibility and structured sparsity of the phonological features. We propose a compressive sampling framework for speech coding and sparse reconstruction for decoding prior to synthesis. Compressive sampling is found to be a principled way for compression in contrast to the conventional pruning approach; it leads to 50% reduction in the bit-rate for better or equal quality of the decoded speech. Furthermore, exploiting the structured sparsity and binary characteristic of these features have shown to enable very low bit-rate coding at 700 bps with negligible quality loss; this coding scheme imposes no latency. If we consider a latency of 256 ms for supra-segmental structures, the rate of 250-350 bps is achieved.

Full Paper

Bibliographic reference.  Asaei, Afsaneh / Cernak, Milos / Bourlard, Hervé (2015): "On compressibility of neural network phonological features for low bit rate speech coding", In INTERSPEECH-2015, 418-422.