电脑知识与技术
電腦知識與技術
전뇌지식여기술
Computer Knowledge and Technology
2015年
20期
141-142
,共2页
数字语音识别%BP神经网络%非线性时间规整%Mel频率倒谱系数
數字語音識彆%BP神經網絡%非線性時間規整%Mel頻率倒譜繫數
수자어음식별%BP신경망락%비선성시간규정%Mel빈솔도보계수
Digital speech recognition%BP neural network%Nonlinear time neat%Mel frequency cepstrum coefficient
研究BP神经网络技术在数字语音识别中的应用,以基于语音信号产生的数字模型作为突破口,对所采集到的语音信号进行预处理,提取Mel频率倒谱系数,并将特征参数序列进行非线性时间规整为固定的帧数以便于BP神经网络的训练和识别.由MATLAB的实验数据分析可得,基于BP神经网络的数字语音识别技术具有很高的实用价值、数字语音识别率高.
研究BP神經網絡技術在數字語音識彆中的應用,以基于語音信號產生的數字模型作為突破口,對所採集到的語音信號進行預處理,提取Mel頻率倒譜繫數,併將特徵參數序列進行非線性時間規整為固定的幀數以便于BP神經網絡的訓練和識彆.由MATLAB的實驗數據分析可得,基于BP神經網絡的數字語音識彆技術具有很高的實用價值、數字語音識彆率高.
연구BP신경망락기술재수자어음식별중적응용,이기우어음신호산생적수자모형작위돌파구,대소채집도적어음신호진행예처리,제취Mel빈솔도보계수,병장특정삼수서렬진행비선성시간규정위고정적정수이편우BP신경망락적훈련화식별.유MATLAB적실험수거분석가득,기우BP신경망락적수자어음식별기술구유흔고적실용개치、수자어음식별솔고.
The BP neural network technology in the application of digital speech recognition,based on the figures of speech signal model as a Breakthrough, Collected for the speech signal preprocessing, The extraction of Mel frequency cepstrum coefficient,and will feature parameters for nonlinear time sequence neat for the fixed frame is advantageous for the BP neural network of training and recognition.By the MATLAB analysis of experimental data available ,digital speech recognition based on BP neural network has a high practical value,digital speech recognition rate is high.
