一種基于GMM模型的語(yǔ)音情感識(shí)別方法

在人機(jī)語(yǔ)音交互系統(tǒng)中，機(jī)器不僅要具有理解人類(lèi)語(yǔ)音的能力，還應(yīng)當(dāng)具有識(shí)別說(shuō)話人情感的能力。本文提出了基于高斯混合模型(GMM)的序列分類(lèi)和識(shí)別的改進(jìn)方法，并將該方法引入到語(yǔ)音情感識(shí)別的研究中。本文提出了觀測(cè)值次序均衡的方法。實(shí)驗(yàn)結(jié)果證明這種新的方法有效地提高了語(yǔ)音情感識(shí)別的準(zhǔn)確率。
關(guān)鍵詞：語(yǔ)音序列分類(lèi)；語(yǔ)音情感識(shí)別；GMM評(píng)分函數(shù)

EMOTION CLASSIFICATION AND RECOGNITION OF SPEECH? USING GMM-BASED METHOD HUANG Feng, YIN Jun-xun (School of Elec.&Info. Engg., South China Univ. of Technology, Guangzhou 510640,China) Abstract: For human-machine speech interaction system, machine should not only have the ability to understand contents of human speech, but should also be able to recognize human emotion from input speech to perform properly. In this paper we improve the sequence classification and recognition method based on GMM scoring and score space mapping, and use the new method for speech emotion classification. We propose order compensation method for improvement. Experiment results show the new method significantly improves accuracy of emotion classification and recognition in speech.
Key words: Speech sequential classification, emotion recognition, GMM scoring