基于深度学习的智能语音问答系统研究

随着人工智能行业的不断发展，智能语音问答技术逐步得到国内外学者的广泛关注和研究，但是语音识别方面仍然存在两个技术瓶颈，第一是语音识别系统，第二是根据识别的语音进行问题的回答。基于此，开展了基于深度学习的智能语音问答系统研究。首先介绍了基于隐马尔科夫模型的语音识别系统，然后研究了基于梅尔频率的语音信号特征提取技术，并建立了声学和语言模型，最后研究了基于GRU算法的问答匹配模型，并基于以上模型开发了智能语音问答系统。经实际实验验证分析，文章所提出的算法在语音识别和问答的准确度方面都相比传统算法具有很高的精确度，本算法具有较大的实用价值。

Research on Question-Answering System with Intelligent Voice Based on Deep Learning

With the continuous development of artificial intelligence industry, the technology of question-answering intelligent voice has been widely concerned and studied by scholars at home and abroad. However, there are two technical bottlenecks in speech recognition. The first lies in the speech recognition system, and the second lies in answering to the recognized voice. In response, this paper develops a question answering system with intelligent voice based on in-depth learning. This paper introduces the speech recognition system based on Hidden Markov Model at first , then studies the extraction technology of speech signal features based on Meier frequency, establishes some acoustic and linguistic models, and finally studies the question-answer matching model based on GRU algorithm. A question answering system has been developed as a result of above study. The experimental results show that the proposed algorithm, with considerable practical value, has higher accuracy in speech recognition and question answering than the traditional algorithm.