Performance Comparison of AR Codebook Training for Speech Processing

摘要

In this paper, different ways of training codebook containing autoregressive (AR) parameter vectors are discussed. The fundamental goal of the discussion is to investigate if the classical approach for training AR-codebooks by clustering line spectral frequencies (LSF) can be improved. To do this, we discuss and evaluate the alternatives in terms of the de-correlated AR-parameters and manifold learning. The different training methods are evaluated using different metrics quantifying the distance between actual power spectral density (PSD) and the estimated PSD from the AR-codebook. The experimental results show that the training on the de-correlated features can improve the performance to some degree compared to the traditional LSF training approach in terms of the Itakura-Saito divergence not in terms of the Kullback-Leibler divergence, the log-spectral distortion and speech distortion.