Customizable cloud-healthcare dialogue system based on LVCSR with prosodic-contextual post-processing

This work presents a customized cloud-healthcare dialogue system design based on large vocabulary continuous speech recognition (LVCSR) with prosodic-contextual post-processing. The customized cloud-healthcare dialogue system includes two parts. The first part is the cloud dialogue management and strategy, which manage and provide the services on demand. The second part is a web-based reminder and a customizable interface, which offer settings of reminding events and the customizable dialogue system. Moreover, for higher accuracy of speech recognition, this work proposes prosodic-contextual post-processing mechanism, which can find the best sentence from potential recognition results by using syllable segmentation, pitch analysis, and contextual analysis. In the experiment, five healthcare scenarios for the elderly are designed for evaluation. The analysis indicates that the average mean opinion score (MOS) can reach as high as 4.23. Additionally, the word error rate (WER) of LVCSR with the proposed prosodic-contextual post-processing is improved by 9.21%. Such results show that the proposed system is suitable for the elderly in daily living and demonstrates feasibility of our idea.