This paper describes a high quality, low bit rate (about 2.4 k bits per sec) speech coder which uses adaptive wavelets to model speech. The optimal wavelet parameters which correspond to the speech model that produced a particular sound are obtained using a feed forward neural network. These parameters are then quantized using scalar and Vector Quantization (VQ) techniques to reduce the number of bits required to transmit a speech signal. Both these quantizers are described in this paper. The bit rate i.e., the number of bits per second of speech for both the quantizers is computed and it has been shown that the bit rate can be reduced signi cantly using a VQ method. The comparative perception results that are obtained by listening to the synthesized speech using both scalar and vector quantized wavelet parameters are reported in this paper. In addition, the change in error obtained by varying the number of wavelets to represent a speech signal is analyzed.
Kadambe REF uses adaptive wavelets for speech scrambling.
Application Of Adaptive Wavelets For Speech Coding
Proceedings of the IEEE-SP International Symposium of Time-Frequency and Time-Scale Analysis
Computer Science