CAJ | 학술논문

针对单一语音特征对语音情感表达不完整的问题，将具有良好量化和插值特性的LSF参数与体现人耳听觉特性的MFCC参数相融合，提出基于线谱权重的MFCC（WMFCC）新特征。同时，通过高斯混合模型来对该参数建立模型空间，进一步得到GW-MFCC模型空间参数，以获取更高维的细节信息，进一步提高情感识别性能。采用柏林情感语料库进行验证，新参数的识别率比传统的MFCC和LSF分别有5.7%和6.9%的提高。实验结果表明，提出的WMFCC以及GW-MFCC参数可以有效地表现语音情感信息，提高语音情感识别率。
침대단일어음특정대어음정감표체불완정적문제，장구유량호양화화삽치특성적LSF삼수여체현인이은각특성적MFCC삼수상융합，제출기우선보권중적MFCC（WMFCC）신특정。동시，통과고사혼합모형래대해삼수건립모형공간，진일보득도GW-MFCC모형공간삼수，이획취경고유적세절신식，진일보제고정감식별성능。채용백림정감어료고진행험증，신삼수적식별솔비전통적MFCC화LSF분별유5.7%화6.9%적제고。실험결과표명，제출적WMFCC이급GW-MFCC삼수가이유효지표현어음정감신식，제고어음정감식별솔。
Aiming the insufficient expression of speech emotion with single type of speech features, a new feature weight-ed MFCC(WMFCC) is proposed combining LSF with good interpolation and quantization performance and MFCC which presents human hearing characters. GMM model is applied to this feature to obtain high level model space parameter GW-MFCC in order to further improve the emotion recognition rate with detailed information. Experiments are carried out on EMO-DB. The correct recognition rates are 5.7% and 6.9% higher than using MFCC and LSF respectively. The experiment results show that the GW-MFCC feature can effectively convey emotional information in speech, thus can improve the performance in the emotion recognition.