**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 technology acts as a "machine's auditory system," enabling machines to convert speech signals into corresponding text or commands by recognizing and understanding them.
In 1952, Davis and his team at the Bell Institute 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. By the 1970s, large-scale research in speech recognition began, with significant progress made in small vocabulary and isolated word recognition. After the 1980s, the focus shifted toward large vocabulary and non-specific continuous speech recognition.
The research approach also evolved significantly. Traditional methods based on template matching were replaced by statistical models. Additionally, neural network technologies were introduced into speech recognition, marking another major advancement.
In the 1990s, while no major breakthroughs occurred in system frameworks, there was considerable progress in the application and commercialization of speech recognition. For example, DARPA, a U.S. Department of Defense program, supported language understanding systems, shifting its focus to natural language processing in the 1990s, particularly for tasks like air travel information retrieval.
China’s speech recognition research began in 1958 when the Chinese Academy of Sciences used electronic tubes to identify 10 vowels. Due to limited resources, development was slow until 1973, when the institute started computer-based speech recognition.
From the 1980s onward, with the growing use of computers and digital signal processing in China, many domestic institutions had the conditions to conduct speech technology research. International speech recognition became a hot topic, leading to increased investment in this field.
In 1986, speech recognition was recognized as an important part of intelligent computer systems. Supported by the “863†program, China organized research on speech recognition technology and held special sessions every two years, marking a new phase in its development.
Since 2009, with the advancement of deep learning and the accumulation of big data, speech recognition technology experienced rapid growth. Deep neural networks (DNN) were applied to acoustic model training, improving accuracy. Microsoft achieved a 30% reduction in speech recognition error rates using DNN, one of the fastest advancements in the past two decades.
Around 2009, most mainstream speech recognition decoders adopted finite state machine (WFST)-based decoding networks, which integrated language models, dictionaries, and acoustic models into a single decoding network, enhancing decoding speed and enabling real-time applications.
With the rapid development of the Internet and mobile devices, vast amounts of text and speech corpora became available, providing rich resources for training language and acoustic models. Building universal large-scale models became feasible.
In speech recognition, the richness and quality of training data are crucial for system performance. However, corpus annotation and analysis require long-term effort. The era of big data has elevated the importance of large-scale corpus accumulation.
Today, speech recognition on mobile devices is among the hottest trends. Voice dialogue robots, assistants, and interactive tools are emerging rapidly. Many internet companies invest heavily in this field to offer convenient voice interaction models and capture user bases.
**Siri**, derived from the CALO program by the U.S. Defense Advanced Research Projects Agency (DARPA), is a digital assistant that allows users to streamline tasks through voice. Originally a text chat service, Siri collaborated with Nuance for voice recognition and was later acquired by Apple in 2010. It can perform tasks like weather forecasts, scheduling, and search, while continuously learning new sounds and intonations.
**Google Now**, launched with Android 4.1, understands user habits and provides relevant information. It now supports Windows and Mac browsers, offering features like email notifications, mileage tracking, and travel-related updates.
**Baidu Voice** provides voice-based search services, supporting map users and personalized searches. It is embedded in Baidu products such as Baidu Mobile Maps.
**Microsoft Cortana**, a virtual assistant on Windows Phone, simulates human-like conversation and adapts to phone themes. It interacts with users, offering calendar management and more.
Speech recognition remains a competitive area globally, with over 70% of AI companies in China focusing on image or speech recognition. Major players like Apple, Google, Microsoft, Amazon, and Facebook have all invested in voice technology.
**Nuance**, a traditional leader in voice recognition, once dominated the market but has faced challenges in recent years. Other companies like Keda Xunfei, Baidu, Jietong, and others have emerged, each contributing to the industry's growth.
Speech recognition technology involves complex processes, including feature extraction, acoustic model training, and language model development. Challenges include speaker variability, background noise, and robustness.
Applications range from command-based systems to large-vocabulary continuous speech recognition, with uses in voice search, navigation, and more.
Overall, speech recognition continues to evolve, driven by technological advancements and increasing demand for seamless human-machine interaction.
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