CAJ | 학술논문

在语音识别技术的研究中,语音端点检测和拒识是语音前后端处理的关键技术.在噪声环境下,传统的过零率和短时能量的端点检测效果会变得很差;频域的端点检测方法虽然较时域的端点检测方法鲁棒性更高,但是它需要进行大量的计算不能很好地满足嵌入式系统.针对嵌入式系统的特点,为提高语音识别能力,提出了基于统计理论的孤立词的端点检测算法,在一个相对较长的时间段内语音信号服从正态分布,而噪音信号主要存在于信号均值的一定方差范围之内.方法既满足了嵌入式系统的计算要求,又有一定鲁棒性.
재어음식별기술적연구중,어음단점검측화거식시어음전후단처리적관건기술.재조성배경하,전통적과령솔화단시능량적단점검측효과회변득흔차;빈역적단점검측방법수연교시역적단점검측방법로봉성경고,단시타수요진행대량적계산불능흔호지만족감입식계통.침대감입식계통적특점,위제고어음식별능력,제출료기우통계이론적고립사적단점검측산법,재일개상대교장적시간단내어음신호복종정태분포,이조음신호주요존재우신호균치적일정방차범위지내.방법기만족료감입식계통적계산요구,우유일정로봉성.
Speech endpoint detection and out-of-vocabulary rejection are two important parts of the whole automatic speech recognition process.The performance of traditional speech endpoint detection based on short-term energy and zero-crossing rate becomes very poor in noisy environments,and sometimes even unable to work.Methods based on frequency-domain need complex computing,and it can not meet embedded systems well.In this paper a new endpoint detection algorithm is presented,which is based on statistical theory for isolated-word.In order to distinguish speech and noise,speech signals are looked as submitting to normal distribution in a relative long duration,and noise signals exist only within 0.62 standard deviation.Using this method,successful endpoint detection is carried out.