Stress Classification Using Subband Based Features (<Special Issue>Special Issue on Speech Information Processing)

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概要

• 論文の詳細を見る

• On research to determine reliable acoustic indicators for the type of stress present in speech, the majority of systems have concentrated on the statistics extracted from pitch contour, energy contour, wavelet based subband features and Teager-Energy-Operator (TEO) based feature parameters. These systems work mostly on pair-wise distinction between stress and neutral speech. Their performance decreases substantially when tested in multi-style detection among many stress categories. In this paper, a novel system is proposed using linear short time Log Frequency Power Coefficients (LFPC) and TFO based non-linear LFPC features in both time and frequency domain. Five-state Hidden Markov Model (HMM) with continuous Gaussian mixture distribution is used. The stress classification ability of the system is tested using data from the SUSAS (Speech Under Simulated and Actual Stress) database to categorize five stress conditions individually. It is found that the performance of linear acoustic features LFPC is better than that of nonlinear TEO based LFPC feature parameters. Results show that, with linear acoustic feature LFPC, average accuracy of 84% and the best accuracy of 95% can be achieved in the classification of the five categories. Results of test of the system under different signal-to-noise conditions show that the performance of the system does not degrade drastically with increase in noise. It is also observed that classification using nonlinear frequency domain LFPC features gives relatively higher accuracy than that using nonlinear time domain LFPC features.

• 社団法人電子情報通信学会の論文
• 2003-03-01

著者

• De Silva

  Department Of Botany Faculty Of Science University Of Ruhuna

• Foo Wei

  School Of Eee Nanyang Avenue Nanyang Technological University

• De Silva

  Department Of Electrical And Computer Engineering

• NWE Tin

  Department of Electrical and Computer Engineering

• De Silva

  Department Of Animal Science University Of Peradeniya

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