特定领域会话智能:基于语音的电子通道系统

W.A.H Weerathunga, G.N Lokugamage, Hariharan V, A.D.N.H Yahampath, D. Kasthurirathna
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引用次数: 0

摘要

本研究探讨了自动语音识别、自然语言理解、神经网络和文本到语音转换的应用,以创建一个特定领域的端到端语音e - channel系统。本研究的新颖思想可以推广到其他任何领域。:出租车应用程序),并建立会话智能系统。该系统使用户能够避免传统的医生预约通道程序的缺点。该系统还具有根据患者症状预测医生专科的能力,并通过强大的神经网络模块提供紧急健康提示。根据斯里兰卡口音创建特定领域的语音识别模型,并处理特定于该领域的上下文(94%的准确率)。提取实体、处理电子通道功能和选择最合适的API由RASA后端完成。神经网络将根据用户输入给出急救和医生专业建议,验证准确率为90%。语音合成模型将以用户首选语言(僧伽罗语、英语或泰米尔语)输出响应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Domain Specific Conversational Intelligence: Voice Based E-Channeling System
In this research the application of Automatic Speech Recognition, Natural Language Understanding, Neural Networks and Text To Speech Conversion is investigated to create a domain specific end to end voice based E-Channeling system. The novel idea in this research can be extended to any other domain(e.g.: Taxi Application) and build a conversational intelligence system. This system enables the user to avoid the shortcomings in the traditional doctor appointment channeling procedures. The system also have the ability to predict the doctor specialization according to the symptoms of the patient and can give emergency health tips by using the powerful Neural Network module. Domain-specific speech recognition model is created according to Sri Lankan accents and handles the context-specific to this domain(94% accuracy). Extracting the entities, handling e-channeling functions and selecting the most suitable API is done by the RASA backend. Neural Network will give the first aid and doctor specialization recommendations according to user input with a validation accuracy of 90%. Speech synthesis model will output the response in user preferred language(Sinhala, English or Tamil).
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