Mandarin recognition and improvement based on CTC criterion
摘要
The cross-entropy criterion of mainstream neural network training classifies and optimizes each frame of acoustic data, while the continuous speech recognition uses the sequence-level transcription accuracy as the performance measurement.For this difference,an end-to-end speech recognition system based on sequence-level transcription is constructed.The phoneme is used as the basic unit to build the model,and the target function of Connectionist Temporal Classification (CTC) is used to improve the structure of Long Short-Term Memory(LSTM) network.The dictionary and language model are introduced in the decoding process,and the tone feature is added to the front end to enrich the acoustic feature.The modeling effect of CTC is improved by using the sequence discrimination training technique.Experimental results show that the recognition efficiency and accuracy of the proposed system is improved,and the Word Error Rate(WER) can be as low as 19.09%±0.16%.