支持向量机声学建模中轨迹矩阵的外积

信号处理 Pub Date : 2004-09-29 DOI:10.1109/MLSP.2004.1422993

R. Anitha, D. S. Satish, C. Sekhar

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引用次数: 13

摘要

在本文中，我们使用支持向量机解决了不同时长语音片段的分类问题。将不同持续时间段映射到固定维度模式的常用方法可能导致丢失分类所需的关键信息。我们提出了一种方法，该方法将语音片段的表示视为多维空间中的轨迹。通过对多维轨迹的矩阵表示进行外积运算得到的固定维模式向量作为支持向量机的输入。对于由多个音素组成的语音片段的声学建模，对每个音素的轨迹矩阵进行外积运算。通过对英语字母e集孤立语音的识别，验证了所提方法的有效性

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Outerproduct of trajectory matrix for acoustic modeling using support vector machines

In this paper, we address the issues in classification of varying duration segments of speech using support vector machines. Commonly used methods for mapping the varying duration segments into fixed dimension patterns may lead to loss of crucial information necessary for classification. We propose a method in which the representation of a segment of speech is considered as a trajectory in a multidimensional space. A fixed dimension pattern vector derived from the outerproduct operation on the matrix representation of a multidimensional trajectory is given as input to the support vector machines. For acoustic modeling of speech segments consisting of multiple phonemes, the outerproduct operation is carried out for the trajectory matrix of each phoneme. The effectiveness of the proposed methods is demonstrated in recognition of isolated utterances of the E-set of English alphabet

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来源期刊

信号处理

自引率

0.00%

发文量

5812

期刊介绍： Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.