Employing Recursive Neural Networks in Voice Question-Answering Systems: A Novel Approach for Sequence Processing

In this study, we propose and validate a novel Recurrent Neural Network (RNN) model designed for handling sequential data in voice question-answering systems. In our system, the RNN effectively processes speech sequences of various lengths and captures valuable dependencies within. Our model undergoes rigorous performance evaluation on standard TIMIT and LibriSpeech datasets, including Word Error Rate (WER), Answer Correct Rate (ARA), and Speech Synthesis Quality (SSQ). The results demonstrate the excellent performance of our model in dealing with speech sequences of varying lengths and practical applications. Through future research, our model will be further improved and can play its value in a broader scenario.