【作者】 王殿胜；

【导师】 李海峰；

【作者基本信息】 哈尔滨工业大学 ， 计算机科学与技术， 2007， 硕士

【摘要】 作为语音识别领域的核心热点技术之一,关键词检出技术(KWS)近年来得到了长足的发展。虽然目前在语音识别领域隐马尔可夫模型(HMM)占主导地位,但就关键词检出技术而言,人工神经网络(ANN),以出色的判别能力、较小的计算量、更高的灵活度,成为重点研究的方向之一。本文研究了一种新型的神经网络—时间集成人工神经网络(TANN),并把它应用在汉语语音的关键词检出中。TANN通过时间集成和帧间集成两个步聚的时延和集成处理,把语音的时序性特征较好地表征出来,很好地解决了神经网络在处理时序分类问题上遭遇的困难,并且巧妙地回避了语音识别中的时间对正问题。本文引入了一种基于熵误差函数(EEF)新颖的网络训练算法,并辅之以增加动量项和变步长学习法等多种加速网络收敛算法。这种训练算法不但通过对样本进行多点学习,使网络适应关键词检出时的动态过程,还有效地加速了时间集成人工神经网络的收敛速度,使网络在学习过程中逃出部分误差曲面的局部极小点。在关键词检出的过程中,本文采用了TANN作为分类器。本文还提出一种多模板联合决策算法,这一算法借鉴了多分类器融合思想,改进了单一分类器在时间集成方面的不足,提高了检出正确率。基于TANN的关键词检出技术,检出正确率最高可达80%以上,并可满足实时应用要求,值得进一步的研究和探索。更多还原

【Abstract】 As one key research field and application hotpot of speech recognition, Keyword Spotting(KWS) technology has made a significant improvement in recent years. Although Hidden Markov Model(HMM) has been mainstream of speech recognition for years, Artificial Neural Network(ANN), with its strong discriminatory ability, low computation cost, high flexibility, has become an efficient solution to KWS.In this paper, we have an investigation into a novel strategy on KWS, in which a new type of time-delayed neural network called Time-Accumulation Neural Network(TANN) is adopted, TANN with two steps, time accumulation and frame accumulation. TANN is quite a solution to the problem both faced in temporal sequence pattern classification and time warping in speech recognition. This paper refers an innovative network training algorithm based on entropy error function(EEF), supplemented by increased momentum items and variable step-learning and other accelerated network convergence algorithms. Such training algorithm is not only through learning at several points to make it adapt dynamic process in KWS, but also effectively accelerate the speed of Artificial Neural Networks convergence, enable the network to escape some of the error surface local minima in the learning process.In KWS, the paper uses TANN as the classifier. The paper also proposes a multi-template joint decision-making theory, it comes classifier fusion theory. It has improved the classification of a single integrated in time for the shortage, increases the correct rate. The KWS technique based on TANN, correct rate can reach more than 80%, and also can satisfy the request of real-time application, worthy of further research and exploration.更多还原