Research on Speech Intonation Recognition Technology Based on Deep Learning in Human-Computer Interaction

Abstract

As a critical subfield of speech signal processing, speech intonation recognition technology aims to interpret paralinguistic features (such as pitch, rhythm, and energy) beyond the textual content of an utterance. Its development provides the core driver for enhancing the naturalness and emotional intelligence of human-computer interaction. This study focuses on intonation recognition technology, a critical component of speech signal processing. Its development has progressed from rule-based to statistical models, and now to deep learning models, resulting in steadily improving recognition accuracy. Regarding feature extraction, the acquisition of speech signal characteristics such as pitch, duration, and volume provides the data foundation for recognition models. Recognition algorithms have evolved from early Hidden Markov Models (HMMs) and Support Vector Machines (SVMs) to current mainstream deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This technology is widely applied in areas such as voice assistants, intelligent customer service, and speech synthesis. For instance, it enables emotion analysis in voice assistants to adjust service strategies and enhances naturalness in synthesized speech. Current research focuses on developing algorithms robust to noise and accent interference while integrating with cognitive science. Future breakthroughs leveraging deep learning are anticipated in model complexity and recognition accuracy. Furthermore, driven by Internet of things and 5G technologies, applications are expected to expand into smart homes, telemedicine, and other domains.