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Titlebook: Automatic Speech Recognition; The Development of t Kai-Fu Lee Book 1989 Springer Science+Business Media New York 1989 N-Gramm.Symbol.artifi

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樓主
發(fā)表于 2025-3-21 16:58:59 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Automatic Speech Recognition
期刊簡稱The Development of t
影響因子2023Kai-Fu Lee
視頻videohttp://file.papertrans.cn/167/166447/166447.mp4
學科分類The Springer International Series in Engineering and Computer Science
圖書封面Titlebook: Automatic Speech Recognition; The Development of t Kai-Fu Lee Book 1989 Springer Science+Business Media New York 1989 N-Gramm.Symbol.artifi
影響因子Speech Recognition has a long history of being one of the difficult problems in Artificial Intelligence and Computer Science. As one goes from problem solving tasks such as puzzles and chess to perceptual tasks such as speech and vision, the problem characteristics change dramatically: knowledge poor to knowledge rich; low data rates to high data rates; slow response time (minutes to hours) to instantaneous response time. These characteristics taken together increase the computational complexity of the problem by several orders of magnitude. Further, speech provides a challenging task domain which embodies many of the requirements of intelligent behavior: operate in real time; exploit vast amounts of knowledge, tolerate errorful, unexpected unknown input; use symbols and abstractions; communicate in natural language and learn from the environment. Voice input to computers offers a number of advantages. It provides a natural, fast, hands free, eyes free, location free input medium. However, there are many as yet unsolved problems that prevent routine use of speech as an input device by non-experts. These include cost, real time response, speaker independence, robustness to variation
Pindex Book 1989
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0893-3405 om problem solving tasks such as puzzles and chess to perceptual tasks such as speech and vision, the problem characteristics change dramatically: knowledge poor to knowledge rich; low data rates to high data rates; slow response time (minutes to hours) to instantaneous response time. These characte
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https://doi.org/10.1007/978-1-84628-915-6 weaknesses of word and phone models, as well as a number of other units proposed by earlier work. Then, we will propose two new units that will substantially improve the performance of speaker-independent continuous speech recognizers. Finally, we will present comparative results of different variations of these units.
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The Baseline SPHINX System, baseline system will use basic HMM techniques utilized by many other systems [Rabiner 83, Bahl 83a, Schwartz 84, Sugawara 85]. We will show that using these techniques alone, we can already attain reasonable accuracies.
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