Performance Comparison of AR Codebook Training for Speech Processing
| Autor: | Changchun Bao, Jesper Kjar Nielsen, Mads Grasboll Christensen, Zihao Cui |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
business.industry Computer science Speech coding Feature extraction Nonlinear dimensionality reduction Codebook Spectral density Pattern recognition 02 engineering and technology Speech processing 030507 speech-language pathology & audiology 03 medical and health sciences 020901 industrial engineering & automation Autoregressive model Distortion Artificial intelligence 0305 other medical science Cluster analysis business Divergence (statistics) |
| Zdroj: | Cui, Z, Bao, C, Nielsen, J K & Christensen, M G 2020, Performance Comparison of AR Codebook Training for Speech Processing . in 2020 15th IEEE International Conference on Signal Processing (ICSP) ., 9320929, IEEE Signal Processing Society, IEEE International Conference on Signal Processing (ICSP), pp. 131-135, 15th IEEE International Conference on Signal Processing, 06/12/2020 . https://doi.org/10.1109/ICSP48669.2020.9320929 |
| DOI: | 10.1109/icsp48669.2020.9320929 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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