Support Vector Machine with Dynamic Time-Alignment Kernel for Speech Recognition

机译：支持向量机与动态时间对齐内核的语音识别

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A new class of Support Vector Machine (SVM) which is applicable to sequential-pattern recognition is developed by incorporating an idea of non-linear time alignment into the kernel. Since time-alignment operation of sequential pattern is embedded in the kernel evaluation, same algorithms with the original SVM for training and classification can be employed without modifications. Furthermore, frame-wise evaluation of kernel in the proposed SVM (DTAK-SVM) enables frame-synchronous recognition of sequential pattern, which is suitable for continuous speech recognition. Preliminary experiments of speaker-dependent 6 voiced-consonants recognition demonstrated excellent recognition performance of more than 98% in correct classification rate, whereas 93% by hidden Markov models (HMMs).

机译：通过将非线性时间对齐的思想纳入内核，开发了一种适用于顺序模式识别的新型支持向量机（SVM）。由于顺序模式的时间对齐操作已嵌入内核评估中，因此可以采用与原始SVM相同的算法进行训练和分类，而无需进行修改。此外，在所提出的SVM（DTAK-SVM）中对内核进行逐帧评估可以实现顺序模式的帧同步识别，这适用于连续语音识别。语音相关的6个辅音识别的初步实验表明，正确分类率的识别性能高达98％以上，而隐马尔可夫模型（HMM）的识别性能高达93％。

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来源
《European Conference on Speech Communication and Technology v.3; 20010903-20010907; Aalborg; DK》|2001年|P.1841-1844|共4页
会议地点 Aalborg(DK);Aalborg(DK)
作者
Hiroshi Shimodaira; Ken-ichi Noma; Mitsuru Nakai; Shigeki Sagayama;
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School of Information Science, Japan Advanced Institute of Science and Technology, Graduate School of Engineering, University of Tokyo;

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正文语种 eng
中图分类传播理论;
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1. Sequential-pattern recognition by support vector machines using dynamic time-alignment kernels [J] . Kenichi Noma, Mitsuru Nakai, Hiroshi Shimodaira, 電子情報通信学会技術研究報告. パターン認識·メディア理解. Pattern Recognition and Media Understanding . 2000,第507期

机译：支持向量机使用动态时间对齐内核进行顺序模式识别
2. Sequential-pattern recognition by support vector machines using dynamic time-alignment kernels [J] . Kenichi Noma, Mitsuru Nakai, Hiroshi Shimodaira, 電子情報通信学会技術研究報告. パターン認識·メディア理解. Pattern Recognition and Media Understanding . 2000,第507期

机译：支持向量机使用动态时间对齐内核的顺序模式识别
3. Enhanced polynomial kernel (EPK)–based support vector machine (SVM) (EPK-SVM) classification technique for speech recognition in hearing-impaired listeners [J] . Pavithra S., Janakiraman S. Concurrency, practice and experience . 2021,第3期

机译：基于增强的多项式内核（EPK）基础支持向量机（SVM）（SVM）（EPK-SVM）听力障碍听众语音识别的分类技术
4. Support Vector Machine with Dynamic Time-Alignment Kernel for Speech Recognition [C] . Hiroshi Shimodaira, Ken-ichi Noma, Mitsuru Nakai, European conference on speech communication and technology . 2001

机译：支持矢量机器与动态时间对齐内核进行语音识别
5. Bird call recognition with artificial neural networks, support vector machines, and kernel density estimation. [D] . Ross, Derek J. 2006

机译：利用人工神经网络，支持向量机和核密度估计进行鸟叫识别。
6. Emotion Recognition from Single-Trial EEG Based on Kernel Fishers Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine [O] . Yi-Hung Liu, Chien-Te Wu, Wei-Teng Cheng, 2014

机译：基于核Fisher情绪模式和不对称拟共形核支持向量机的单次EEG情绪识别
7. Dynamic Time-Alignment Kernel in Support Vector Machine. [O] . Shimodaira Hiroshi, Noma Ken ichi, Nakai Mitsuru, 2001

机译：支持向量机中的动态时间对齐内核。

Support Vector Machine with Dynamic Time-Alignment Kernel for Speech Recognition

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