**Development of Speech Recognition Technology**
Communicating with machines and letting them understand what you're saying has become a reality thanks to speech recognition technology. This innovation acts like a "machine's auditory system," enabling a machine to convert speech signals into corresponding text or commands through recognition and understanding.
In 1952, at the Bell Institute, Davis and his team developed the world's first experimental system capable of recognizing 10 English digits. In the 1960s, Denes from the UK created the first computer-based speech recognition system. The 1970s marked the beginning of large-scale research in speech recognition, with significant progress made in small vocabulary and isolated word recognition. By the 1980s, the focus shifted to large vocabulary and continuous speech recognition.
The field underwent major changes in research approaches. Traditional methods based on template matching were replaced by statistical models. Later, neural networks were introduced to enhance accuracy and adaptability.
In the 1990s, while no major breakthroughs occurred in system architecture, significant progress was made in the application and commercialization of speech recognition. Programs like DARPA supported the development of language understanding systems, focusing on natural language processing and tasks such as air travel information retrieval.
China's speech recognition research began in 1958, when the Chinese Academy of Sciences' Institute of Acoustics used electronic circuits to recognize 10 vowels. Due to technological limitations, progress was slow until 1973, when the institute started computer-based speech recognition.
With the spread of computer applications and digital signal processing in China during the 1980s, many domestic institutions gained the conditions to conduct speech technology research. International interest in speech recognition grew, leading to increased investment in this field.
In 1986, speech recognition was included in the research of intelligent computer systems. With support from the "863" program, China organized research on speech recognition technology and held specialized sessions every two years, marking a new stage in its development.
Since 2009, advancements in deep learning and big data have propelled speech recognition forward. Deep neural networks improved acoustic model accuracy, with Microsoft reducing error rates by 30% using DNNs. Around 2009, most mainstream decoders adopted WFST-based decoding networks, improving real-time performance.
With the rise of the internet and mobile devices, vast amounts of speech and text corpora became available, enhancing language and acoustic model training. Large-scale models became possible, driven by big data accumulation.
Speech recognition now dominates mobile applications, with voice assistants like Siri, Google Now, Baidu Voice, and Microsoft Cortana leading the way. These tools offer natural interaction, helping users with daily tasks.
Siri, originally developed by Siri Inc., was acquired by Apple in 2010. It uses natural language input to perform actions like weather checks and scheduling. Google Now, launched with Android 4.1, offers personalized information based on user behavior. Baidu Voice provides voice search services, supporting multiple platforms.
Microsoft Cortana, a virtual assistant, helps with tasks like calendar management and location-based recommendations. Amazon's Alexa and Facebook’s voice technologies also play key roles in smart home and communication.
Nuance, a traditional leader in speech recognition, once dominated the market but faces competition from newer players. Other companies like Keda Xunfei, Baidu, and others have emerged, contributing to the growing industry.
Speech recognition involves complex processes, including feature extraction, acoustic modeling, and language modeling. Challenges include speaker variability, background noise, and robustness. Despite these difficulties, the technology continues to evolve, offering more accurate and efficient solutions for everyday use.
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