Pattern Recognition in Speech and Language Processing
Pattern Recognition in Speech and Language Processing
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
A New Optimization Engine for the LSF Vector Quantization
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03
A novel lossless data compression scheme based on the error correcting Hamming codes
Computers & Mathematics with Applications
LSP-based multiple-description coding for real-time low bit-rate voice over IP
IEEE Transactions on Multimedia
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In this paper, a replacement algorithm for Linear Prediction Coefficients (LPC) along with Hamming Correction Code based Compressor (HCDC) algorithms are investigated for speech compression. We started with an CELP system with order 12 and with Discrete Cosine Transform (DCT) based residual excitation. Forty coefficients with transmission rate of 5.14 kbps were first used. For each frame of the testing signals we applied a multistage HCDC, we tested the compression performance for parities from 2 to 7, we were able to achieve compression only at parity 4. This rate reduction was made with no compromise in the original CELP signal quality since compression is lossless. The compression approach is based on constructing dynamic reflection coefficients codebook, this codebook is constructed and used simultaneously using a certain store/retrieve threshold. The initial linear prediction codec we used is excited by a discrete cosine transform (DCT) residual, the results were tested using the MOS and SSNR, we had acceptable ranges for the MOS (average 3.6), and small variations of the SSNR (卤5 db).