結合情緒關鍵字以提升語音情緒辨識率之研究

A Study on the Combination of Emotion Keyword to Improve the Speech Emotion Recognition Accuracy

黃文怡(Wen-Yi Huang)

指導教授 ： 包蒼龍

大同大學/電機資訊學院/資訊工程學系所/碩士(2012年)

摘要

語音情緒辨識在人類情緒的研究中是一個重要的項目。我們提出一個方法，藉由融合情緒關鍵字分析與語音情緒辨識來提升辨識率的方法。利用語音情緒辨識模型來提取語音訊號的特徵，以及使用分類器去分類出情緒類別。我們對輸入的語音訊號提取梅爾頻率倒頻譜係數 (MFCC)，並採用權重離散K鄰近點 (WD-KNN)以及支持向量機 (SVM)去分類情緒的類別。在情緒關鍵字分析部分，我們從劇本中提取情緒關鍵字，經由標記者去手動標記情緒關鍵字的情緒類別以及強度值。最後我們融合情緒關鍵字與語音情緒辨識模型的參數來提升系統的辨識率。根據實驗結果，憤怒情緒語句的辨識率提升3%, 非生氣語句誤判成生氣語句的錯誤率減少了40%.

關鍵字

情緒關鍵字 ； 語音情緒辨識

並列摘要

The speech emotion recognition is one of the important researches on the discovery of human emotion. In this thesis, we proposed a method which fuses the results of the keyword analysis and speech emotion recognition to improve the recognition rate. The speech emotion recognition model extracts features of speech signal and uses a proper classifier to classify the emotion. In this research, we use the Mel-Frequency Cepstral Coefficients (MFCC) extracted from the input speech as the feature for classification. The weighted discrete K-Nearest Neighbor (WD-KNN) and Support Vector Machine (SVM) classifiers are adopted to classify the emotion in the speech. In emotion keyword analysis, the emotion keywords were selected from the scripts. The emotion keyword category and intensity are manually defined by annotators. Finally, we fuse the results of the speech emotion recognition model and the emotion keyword analysis to improve the recognition rate. Experimental results show that the recognition rate increases 3% with the fusing model as compared to the use of the speech emotion recognition along. The incorrect rate of misclassifying non-anger to anger is reduced by 40% .

並列關鍵字

emotion keyword ； speech emotion recognition

參考文獻

[13] Ze-Jing Chuang and Chung-Hsien Wu, "Multi-Modal Emotion Recognition from Speech and Text," Computational Linguistics and Chinese Language Processing.

[1] A. Ortony and T. J. Turner, "What's Basic about Basic Emotions," vol. 97, no. 3, pp. 315-331, 1990.

[3] R.S. Lazarus, Stress and Emotion: A New Synthesis, London: Free Association Books, 1999.

[5] Yi-Hsuan Yang, Yu-Ching Lin, Ya-Fan Su, and Chen, H.H., "Music Emotion Classification: A Regression Approach," in ICME, 2007.

[7] J. Yeh, Emotion Recognition from Mandarin Speech Signals, Master Thesis, Tatung University, June 2004.