自動化外文翻譯---改進型智能機器人的語音識別方法(文件)
【正文】 ns will be recognized as the resulted words and the accuracy can be evaluated. Thewhole process is illustrated in Fig. 1. Fig. 1 Block diagram of speech recognition system 3 Theory andmethod Extraction of speaker independent features from the speech signal is the fundamental problem of speaker recognition system. The standard methodology for solving this problem uses Linear Predictive Cepstral Coefficients (LPCC) and MelFrequency Cepstral Coefficient (MFCC). Both these methods are linear procedures based on the assumption that speaker features have properties caused by the vocal tract resonances. These features form the basic spectral structure of the speech signal. However, the nonlinear information in speech signals is not easily extracted by the present feature extraction methodologies. So we use fractal dimension to measure non2linear speech turbulence. This paper investigates and implements speaker identification system using both traditional LPCC and nonlinear multiscaled fractal dimension feature extraction. 3. 1 L inear Predictive Cepstral Coefficients Linear prediction coefficient (LPC) is a parameter setwhich is obtained when we do linear prediction analysis of speech. It is about some correlation characteristics between adjacent speech samples. Linear prediction analysis is based on the following basic concepts. That is, a speech sample can be estimated approximately by the linear bination of some past speech samples. According to the minimal square sum principle of difference between real speech sample in certain analysis frame shorttime and predictive sample, the only group ofprediction coefficients can be determined. LPC coefficient can be used to estimate speech signal cepstrum. This is a special processing method in analysis of speech signal shorttime cepstrum. System function of channelmodel is obtained by linear prediction analysis as follow. Where p represents linear prediction order, ak,(k=1,2,…,p) represent sprediction coefficient, Impulse response is represented by h(n). Suppose cepstrum of h(n) is represented by ,then (1) can be expanded as (2). The cepstrum coefficient calculated in the way of (5) is called LPCC, n represents LPCC order. When we extract LPCC parameter before, we should carry on speech signal preemphasis, framing processing, windowingprocessing and endpoints detection etc. , so the endpoint detection of Chinese mand word“Forward”is shown in , next, the speech waveform ofChinese mand word“Forward”and LPCC parameter waveform after Endpoint detection is shown in Fig. 3. 3. 2 Speech Fractal Dimension Computation Fractal dimension is a quantitative value from the scale relation on the meaning of fractal, and also a measuring on selfsimilarity of its structure. The fractal measuring is fractal dimension[67]. From the viewpoint of measuring, fractal dimension is extended from integer to fraction, breaking the limitof the general to pology set dimension being integer Fractal dimension,fraction mostly, is dimension extension in Euclidean geometry. There are many
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