Automatic Speech Recognition (ASR) technology has become a core technology in the field of artificial intelligence. It enables computers to execute commands or convert speech into text by recognizing and understanding human voices. In recent years, thanks to the rapid development of deep learning and artificial intelligence technologies, significant progress has been made in speech recognition, especially in terms of accuracy, speed, and applicability. Modern speech recognition systems can recognize speech with accuracy close to that of humans and are capable of handling various languages, dialects, accents, and voices in noisy environments.
Current research focuses include, but are not limited to, improving recognition performance for low-resource languages, reducing dependence on labeled data through self-supervised learning methods, enhancing generalization across domains and languages, and achieving more efficient real-time speech recognition systems. Furthermore, the application scenarios of speech recognition are continuously expanding, extending from traditional speech-to-text and voice command recognition to more complex tasks such as sentiment analysis, social signal processing, and voice generation.
To adapt to the complex and changing application environments, speech recognition technology is evolving towards more intelligent, personalized, and context-aware directions. For instance, by integrating context understanding and multimodal information processing, speech recognition systems can more accurately grasp users' intentions and emotional states, providing a richer and more personalized interactive experience. In terms of hardware, with the development of edge computing technology, an increasing number of speech recognition functions are being integrated into smart terminal devices. This not only reduces dependence on cloud processing and lowers latency but also enhances the privacy and security of user data.