自動(dòng)化外文文獻(xiàn)英文文獻(xiàn)外文翻譯改進(jìn)型智能機(jī)器人的語音識(shí)別方法-展示頁
2025-07-04 03:35本頁面
【正文】 recognition has received more and more attention recently due to the important theoretical meaning and practical value [5 ]. Up to now, most speech recognition is based on conventional linear system theory, such as Hidden Markov Model (HMM) and Dynamic Time Warping(DTW) . With the deep study of speech recognition, it is found that speech signal is a plex nonlinear process. If the study of speech recognition wants to break through, nonlinearsystem theory method must be introduced to it. Recently, with the developmentof nonlineasystem theories such as artificial neural networks(ANN) , chaos and fractal, it is possible to apply these theories to speech recognition. Therefore, the study of this paper is based on ANN and chaos and fractal theories are introduced to process speech recognition.Speech recognition is divided into two ways that are speaker dependent and speaker independent. Speaker dependent refers to the pronunciation model trained by a single person, the identification rate of the training person?sorders is high, while others’orders is in low identification rate or can’t be recognized. Speaker independent refers to the pronunciation model trained by persons of different age, sex and region, it can identify a group of persons’orders. Generally, speaker independent system ismorewidely used, since the user is not required to conduct the training. So extraction of speaker independent features from the speech signal is the fundamental problem of speaker recognition system.Speech recognition can be viewed as a pattern recognition task, which includes training and , speech signal can be viewed as a time sequence and characterized by the powerful hidden Markov model (HMM). Through the feature extraction, the speech signal is transferred into feature vectors and act asobservations. In the training procedure, these observationswill feed to estimate the model parameters of HMM. These parameters include probability density function for the observations and their corresponding states, transition probability between the states, etc. After the parameter estimation, the trained models can be used for recognition task. The input observations 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 system3 Theory andmethodExtraction 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 fea
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