Abstract: Currently,attention mechanism based sequence-to-sequence acoustic models has become a hotspot of speech recognition.In view of the problem of long training time and poor robustness,this paper proposed an acoustical model combining bottleneck features.The model is composed of the bottleneck feature extraction network based on deep belief network and the attention-based sequence-to-sequence model.DBN introduces the priori information of the traditional acoustic model to speed up the model convergence rate and enhance robustness and distinction of bottleneck feature.Attention model uses the timetemporal information of voice feature sequence to calculate the posterior probability of phoneme sequence.On the basis of the baseline system,the training time is decreased by reducing the layer number of the recurrent neural network in the attention model,and the recognition accuracy is optimized by changing the input dims and outputs of the bottleneck feature extraction network.Experiments on TIMIT dataset show that in the core test set,the phoneme error rate decreases to 17.80%,the average time training time during an iteration decreases by 52%,and the epochs of training iterations decreases to 89 from 139.

Key words: Acoustic model, Attention model, Bottleneck feature, Deep belief network