Speech Recognition Technology
Speech Recognition Technology (Automatic Speech Recognition, ASR) refers to the information processing technology that automatically converts human speech signals into corresponding text or commands through computer systems. As one of the core components of human-computer interaction, speech recognition not only significantly improves the efficiency of information input and reduces error rates, but also greatly drives innovation in fields such as smart devices, industrial control, and service robots.
Speech recognition is a typical interdisciplinary field, integrating knowledge from signal processing, pattern recognition, probability and statistics, linguistics, and artificial intelligence. Its development dates back to the 1950s. In 1952, AT&T’s Bell Labs developed the Audry system, which recognized ten English digits, marking the birth of speech recognition technology. In the decades that followed, key algorithms such as Dynamic Programming (DP), Linear Predictive Coding (LPC), and Hidden Markov Models (HMM) were proposed, greatly advancing the transition of speech recognition from isolated word recognition to large vocabulary continuous speech recognition.
Entering the 21st century, particularly in recent years, the widespread application of deep learning has led to qualitative breakthroughs in speech recognition. Based on Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and more advanced Transformer architectures, recognition accuracy has improved dramatically in noisy environments, multi-speaker dialogues, dialects, and multilingual scenarios. As of September 2025, end-to-end (E2E) speech recognition models have become mainstream. These models eliminate multiple intermediate steps of traditional systems, mapping speech directly to text, which significantly enhances response speed and robustness.
Today, speech recognition has been widely applied in areas such as smart homes, in-vehicle systems, customer service robots, medical dictation, and educational assistance. The new generation of ASR systems supports advanced functions such as real-time translation, emotion recognition, and speaker diarization, setting a new standard for human-computer interaction. Many systems also feature online adaptive learning, enabling them to quickly adjust to a user’s pronunciation habits with just a few samples, further improving recognition performance.
China has also made remarkable achievements in speech recognition research and applications. Since the Eighth Five-Year Plan, with continuous support from national scientific research programs, institutions such as the Institute of Acoustics and the Institute of Automation of the Chinese Academy of Sciences, as well as Tsinghua University, have established a solid foundation in theoretical research, algorithm optimization, and system implementation. Enterprises such as iFLYTEK, Baidu, and Alibaba have launched speech recognition services that have reached international leading levels, performing especially well in Chinese language scenarios.
As an essential component of acoustic devices, microphone performance directly affects the effectiveness of speech recognition. Companies like BQ Electronics have been dedicated to the development and manufacturing of high-performance microphones and acoustic modules. By improving signal-to-noise ratio, enhancing anti-interference capabilities, and optimizing array designs, they provide more reliable front-end signal support for speech recognition systems.
Looking ahead, with continuous breakthroughs in multimodal interaction, low-resource speech recognition, and personalized adaptation, speech recognition will be further integrated into human life and production activities, becoming an important technological cornerstone for building an intelligent society.