Abstract: The end-to-end framework is a probability model based on the depth neural network which can directly predict the speech signal and the target language character.From the original data input to the result output,the intermediate processing process and neural network are integrated,which can be separated from human subjective bias,directly extract the features,fully mine the data information,and simplify the task processing steps.In recent years,with the introduction of attention mechanism,the auxiliary end-to-end architecture realizes the mutual mapping between multimode,further improving the overall performance of the technology.Through the research on the technology and application of end-to-end technology in the field of intelligent speech in recent years,the end-to-end architecture provides a new idea and method for speech model algorithm,but there are also problems such as the mixed framework can not effectively balance and take into account the single technical characteristics,the complexity of the internal logic of the model makes it difficult for human intervention debugging,and the customization scalability is weakened.In the future,there will be further development in the application of the end-to-end integrated model in the field of speech.On the one hand,the front-end to back-end modules ignore the multiple input assumptions in front-end speech enhancement and back-end speech recognition to integrate speech enhancement and acoustic modeling.On the other hand,the end-to-end interactive information carrier focuses on the information extraction and processing of speech signal data itself the human-compu-ter interaction is closer to the real human language communication.

Key words: End-to-end model, Human-computer interaction, Hybrid framework, Intelligent voice