外文翻譯--基于仿生模式識(shí)別的非特定人連續(xù)語(yǔ)音識(shí)別系統(tǒng)-文庫(kù)吧資料
2025-05-22 07:25本頁(yè)面
【正文】 ts were also carried on to evaluate Continuous density hidden Markov models(CDHMM ),Dynamic time warping(DTW)and BPR for speech recognition． The Experiment results show that BPR outperforms CDHMM and DTW especially in the cases of samples of a finite size． Key words—Biomimetic pattern recognition, Speech recogniton,Hidden Markov models(HMMs),Dynamic time warping(DTW)． I． Introduction The main goal of Automatic speech recognition(ASR)is to produce a system which will recognize accurately normal human speech from any speaker． The recognition system may be classified as speakerdependent or speakerindependent． The speaker dependence requires that the system be personally trained with the speech of the person that will be involved with its operation in order to achieve a high recognition rate． For applications on the public facilities， on the other hand， the system must be capable of recognizing the speech uttered by many different people， with different gender， age， accent,etc.,the speaker independence has many more applications， primarily in the general area of public facilities． The most diffused technology in speakerindependent speech recognition is Hidden Markov Models， the disadvantage of it is not only the need of many more training samples， but also long train time requirement． Since Biomimetic pattern recognition(BPR) was first proposed by Wang Shoujue， it has already been applied to object recognition, face identification and face recognition etc.,and achieved much better performance． With some adaptations， such modeling techniques could be easily used within speech recognition 第 2 頁(yè) too． In this paper， a realtime mandarin speech recognition system based on BPR is proposed， which outperforms HMMs especially in the cases of samples of a finite size． The system is a small vocabulary speaker independent continuous speech recognition one. The whole system is implemented on the PC under windows98／ 2020／ XP environment with CASSANNII supports standard 16bit sound card． II． Introduction of Biomimetic Pattern Recognition and Multi— Weights Neuron Networks 1． Biomimetic pattern recognition Traditional Pattern Recognition aims at getting the optimal classification of different classes of sample in the feature space． However, the BPR intends to find the optimal coverage of the samples of the same type. It is from the Principle of Homology—Continuity， that is to say， if there are two samples of the same class, the difference between them must be gradually changed． So a gradual change sequence must be exists between the two samples. In BPR theory． the construction of the sample subspace of each type of samples depends only on the type itself． More detailedly， the construction of the subspace of a certain type of samples depends on analyzing the relations between the trained types of samples and utilizing the methods of “coverage of objects with plicated geometrical forms in the multidimensional space”. 2． Multiweights neuron and multiweights neuron works A Multiweights neuron can be described as follows： 1 2 mY =f [ ( , , , ) ]W W W X ???… ，,Where： 1 2 m,W W… ， are mweights vectors； X is the input vector； ? is the neuron’s putation function； ? is the threshold； f is the activation function． According to dimension theory, in the feature space nR ， nXR? ， the function 1 2 m( , , , )W W W X? … ， = ? construct a (n1)dimensional hypersurface in ndimensional space which is determined by the weights 1 2 m,W W W… ， .It divides the ndimensional space into two parts． If 1 2 m( , , , )W W W X ???… ， is a closed hypersurface, it constructs a finite subspace． According to the
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