臺灣博碩士論文加值系統

語言是溝通的橋樑，在社會中扮演著重要的角色。因此如何正確的表達情感是人際溝通中重要的一環。在本篇論文裡，我們嘗試去實作一個連續中文語音之情緒辨識的系統。在實驗過程中，選擇了梅爾頻率倒頻譜係數 (MFCC) 和線性預測倒頻譜係數 (LPCC) 為特徵參數值。 情緒資料庫包含了5 種情緒，分別是生氣，快樂，悲傷，厭煩，和一般。此外，利用端點偵測來對連續語音進行切割。分類器則是使用權重式離散型最近鄰居演算法。 在這研究過程中，連續的句子由短時距句子所組成。這些合成句子的辨識率為83%。

Language is a mean of communication and speech plays an important role in the society. So how to express emotion correctly is an important aspect in communication. In this thesis, emotion recognition from continuous Mandarin speech signal is implemented. In the experiment, Mel-Frequency Cepstral Coefficients (MFCC) and Linear Prediction Cepstral Coefficients (LPCC) are selected as the features used in the recognition. Five emotions are investigated, including anger, happiness, sadness, boredom, and neutral.
Endpoint detection is tried to segment the continuous speech. Weighted discrete K-nearest neighbor method is chosen as the classifier. In this research, the continuous sentences are composed by several short sentences with known emotion. The average recognition accuracy is 83% for these sentences.

CHAPTER 1 INTRODUCTION
1.1 Introduction
1.2 Motivation and Objective
1.3 Thesis Organization
CHAPTER 2 BACKGROUND
2.1 Emotion Category
2.2 Related Works on Emotional Speech Recognition
CHAPTER 3 CONTINUOUS SPEECH SEGMENTATION AND EMOTION RECOGNITION
3.1 Preprocessing
3.2 Continuous Speech Segmentation
3.3 Speech Database
3.4 Emotion Recognition
CHAPTER 4 EXPERIMENTAL RESULT
4.1 Experimental Environment
4.2 Experimental Results with Uniform Segmentation
4.3 Experimental Results of Segmentation with Silence
4.4 Experimental Results Using Segmentation with Endpoint Dtection
4.5 Recognition of Continuous Speech
CHAPTER 5 CONCLUSION AND FUTURE WORK
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