Research On TANN Oriented To KWS In Chinese Speech

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.