In order to improve the targets for training acoustic model which cannot reflect the nature of speech exactly,a new kind of target obtained by forward backward algorithm was proposed.In the proposed target,a speech frame was aligned to several adjacent states with different probabilities.The new target improved the robustness of the model,as could describe the transition boundary and reflect the nature of speech much more exactly.Meanwhile,for a trade off between the model robustness and the distinction among modeling units,the targets obtained by forward backward algorithm were windowed.The experiments were carried out on Mandarin conversational speech recognition in the customer service domain. In the experiments,a small set of training data were used to verify the importance of the targets in the training and determine the parameter of the window length.Finally,the durations of training data were increased to 60,80 and 100 hours.The results showed that the proposed system achieved consistent improvements,and the relative character error rate reduction ranged from 1.10% to 3.65%.All of the experiments verified the effectiveness of the proposed target.
