本文選題：語音識(shí)別 + MFCC�。� 參考：《西南石油大學(xué)》2014年碩士論文

【摘要】：隨著當(dāng)今科技的高速發(fā)展,語音識(shí)別技術(shù)被越來越多的人所關(guān)注。語音識(shí)別技術(shù)作為智能機(jī)器人研究領(lǐng)域的一個(gè)重要分支,其目的就是讓機(jī)器能夠聽懂人類的語言,便于人機(jī)交流。因此,將語音識(shí)別技術(shù)應(yīng)用于機(jī)器人控制領(lǐng)域,體現(xiàn)了當(dāng)今自動(dòng)化的發(fā)展水平。 本論文基于語音識(shí)別技術(shù),以實(shí)現(xiàn)機(jī)器人的簡(jiǎn)單運(yùn)動(dòng)控制為目標(biāo),完成對(duì)特定人孤立詞語音信號(hào)的識(shí)別,重點(diǎn)研究語音信號(hào)的特征參數(shù)提取算法和語音識(shí)別算法。 結(jié)合語音識(shí)別的基礎(chǔ)理論知識(shí),對(duì)采集到的語音信號(hào)進(jìn)行時(shí)域和頻率域的分析以及歸一化、預(yù)加重、加窗分幀、端點(diǎn)檢測(cè)等預(yù)處理變換。以梅爾頻率倒譜系數(shù)(MFCC)作為語音信號(hào)的特征參數(shù)序列,并將MFCC與小波變換相結(jié)合,獲得更能有效表征語音信號(hào)特征的WT-MFCC參數(shù)。 對(duì)比目前幾種常用語音識(shí)別算法的特點(diǎn),選擇動(dòng)態(tài)時(shí)間規(guī)整(DTW)算法和神經(jīng)網(wǎng)絡(luò)算法對(duì)語音信號(hào)進(jìn)行識(shí)別。對(duì)DTW算法和BP神經(jīng)網(wǎng)絡(luò)算法進(jìn)行深入研究,使用MATLAB編寫基于DTW和BP神經(jīng)網(wǎng)絡(luò)的語音識(shí)別程序,對(duì)比識(shí)別效果并改進(jìn)識(shí)別算法。 在LabVIEW中編寫語音信號(hào)采集程序以及語音識(shí)別的上位機(jī)界面,在Proteus中搭建下位機(jī)直流電機(jī)正反轉(zhuǎn)控制電路,通過虛擬串口技術(shù),完成純軟件環(huán)境下的語音識(shí)別控制系統(tǒng)的仿真。 通過上位機(jī)語音識(shí)別系統(tǒng)對(duì)采集到的語音命令進(jìn)行識(shí)別,編寫MT-UROBOT機(jī)器人的運(yùn)動(dòng)控制程序,采用DTD462無線通信模塊進(jìn)行無線通信,實(shí)現(xiàn)對(duì)MT-UROBOT機(jī)器人的語音控制。
[Abstract]:With the rapid development of science and technology, more and more people pay attention to speech recognition technology. As an important branch of intelligent robot research, speech recognition technology aims at enabling machines to understand human language and facilitate human-computer communication. Therefore, the application of speech recognition technology in robot control field reflects the development level of automation. Based on speech recognition technology, this paper aims at the realization of simple motion control of robot, accomplishes the speech signal recognition of isolated words of a specific person, and focuses on the feature parameter extraction algorithm and speech recognition algorithm of speech signal. Combined with the basic theory of speech recognition, the speech signals collected are analyzed in time domain and frequency domain, and the preprocessing transformation, such as normalization, pre-weighting, windowed frame splitting, endpoint detection and so on, is carried out. Using Mel frequency cepstrum coefficient (MFCC) as the characteristic parameter sequence of speech signal and combining MFCC with wavelet transform, the WT-MFCC parameters which can represent the feature of speech signal more effectively are obtained. Compared with the characteristics of several commonly used speech recognition algorithms, dynamic time warping (DTW) algorithm and neural network algorithm are selected to recognize the speech signal. The DTW algorithm and BP neural network algorithm are deeply studied. The speech recognition program based on DTW and BP neural network is compiled by MATLAB. The recognition effect is compared and the recognition algorithm is improved. The speech signal acquisition program and the upper computer interface of speech recognition are written in LabVIEW, and the forward and backward control circuit of DC motor is built in Proteus. The simulation of speech recognition control system under the pure software environment is completed through virtual serial port technology. Through the speech recognition system of the upper computer, the voice command is recognized, the motion control program of the MT-UROBOT robot is compiled, and the wireless communication module of DTD462 is used to realize the speech control of the MT-UROBOT robot.
【學(xué)位授予單位】：西南石油大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP242;TN912.34

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本文編號(hào)：1810586