Subspace filtering approach based on orthogonal projection for better analysis of stressed speech under clean and noisy environments

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• 作者：Bhanu Priya ; S. Dandapat
• 关键词：Stressed speech ; Stress normalization ; Orthogonal projection ; K ; means clustering ; SVD ; Decorrelation ; HLDA ; Semi ; tied adaptive training
• 刊名：International Journal of Speech Technology
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：19
• 期：4
• 页码：731-742
• 全文大小：792 KB
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing
  Social Sciences
  Artificial Intelligence and Robotics
  
• 出版者：Springer Netherlands
• ISSN：1572-8110
• 卷排序：19

文摘

This study explores a novel subspace projection-based approach for analysis of stressed speech. Studies have shown that stress influences the speech production system and it results in a large acoustic variation between the neutral and the stressed speech. This degrades the discrimination capability of an automatic speech recognition system trained on neutral speech when tested on stressed speech. An effort is made to reduce the acoustic mismatch by explicitly normalizing the stress-specific attributes. The stress-specific divergences are normalized by exploiting the subspace filtering technique. To accomplish this, an orthogonal projection based linear relationship between the speech and the stress information has been explored to filter an effective speech subspace, which consists of speech information. Speech subspace is constructed using K-means clustering followed by singular value decomposition method using neutral speech data. The speech and the stress information are separated by projecting the stressed speech orthogonally onto an effective speech subspace. Experimental results indicate that, the bases of an effective subspace comprises the first few eigenvectors corresponding to the highest eigenvalues. To further improve the system performance, both the neutral and the stressed speech are projected onto the lower dimensional subspace. The projections derived using the neutral speech employs heteroscedastic linear discriminant analysis in maximum likelihood linear transformations-based semi-tied adaptation framework. Consistent improvements are noted for the proposed technique in all the discussed cases.