派博傳思國際中心

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

作者: Nixon 時間: 2025-3-21 16:58
書目名稱Automatic Speech Recognition影響因子(影響力)

書目名稱Automatic Speech Recognition影響因子(影響力)學科排名

書目名稱Automatic Speech Recognition網(wǎng)絡公開度

書目名稱Automatic Speech Recognition網(wǎng)絡公開度學科排名

書目名稱Automatic Speech Recognition被引頻次

書目名稱Automatic Speech Recognition被引頻次學科排名

書目名稱Automatic Speech Recognition年度引用

書目名稱Automatic Speech Recognition年度引用學科排名

書目名稱Automatic Speech Recognition讀者反饋

書目名稱Automatic Speech Recognition讀者反饋學科排名

作者: 文藝 時間: 2025-3-21 23:02

作者: Ccu106 時間: 2025-3-22 02:57
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

作者: 下船 時間: 2025-3-22 08:34
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.

作者: Affirm 時間: 2025-3-22 11:03

作者: 異端 時間: 2025-3-22 13:28

作者: 言外之意 時間: 2025-3-22 20:20

作者: 勤勞 時間: 2025-3-22 23:00

作者: sorbitol 時間: 2025-3-23 02:25

作者: 盡忠 時間: 2025-3-23 07:22
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.

作者: Functional 時間: 2025-3-23 10:52
Conclusion,ov modeling, a powerful mathematical learning paradigm. We also decided to use vector quantization and discrete HMMs for expedience and practicality. Then we attacked the problems of large vocabulary, speaker independence, and continuous speech within our discrete HMM framework.

作者: 老人病學 時間: 2025-3-23 16:55

作者: 假裝是你 時間: 2025-3-23 18:55
Local Performance Improvements,ognition independently at CMU [Baker 75a] and IBM [Bakis 76, Jelinek 76]. It was only in the past few years, however, that HMMs became the predominant approach to speech recognition, superseding dynamic time warping.

作者: Gratuitous 時間: 2025-3-24 01:25
Changes for Global Products and PLM, 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.

作者: Mangle 時間: 2025-3-24 06:00
Changes for Global Products and PLM,ov modeling, a powerful mathematical learning paradigm. We also decided to use vector quantization and discrete HMMs for expedience and practicality. Then we attacked the problems of large vocabulary, speaker independence, and continuous speech within our discrete HMM framework.

作者: Cryptic 時間: 2025-3-24 07:53

作者: 無法取消 時間: 2025-3-24 12:41

作者: 僵硬 時間: 2025-3-24 17:28

作者: neutralize 時間: 2025-3-24 19:46
Task and Databases,We will be evaluating SPHINX on the . task [Price 88]. This task was designed for inquiry of naval resources, but can be generalized to database query. It was created to evaluate the recognizers of the recent DARPA projects, for example, CMU’s speaker-independent ANGEL system [Adams 86], and BBN’s speaker-dependent BYBLOS system [Chow 87].

作者: 我邪惡 時間: 2025-3-25 01:06

作者: AVOW 時間: 2025-3-25 04:08
Summary of Results,Figure 8-1 shows improvements from earlier versions of SPHINX. The seven versions in Figure 8-1 correspond to the following descriptions with incremental improvements:

作者: 錫箔紙 時間: 2025-3-25 09:15
https://doi.org/10.1007/978-1-4615-3650-5N-Gramm; Symbol; artificial intelligence; behavior; cognition; complexity; grammar; hidden Markov model; int

作者: 威脅你 時間: 2025-3-25 13:16
978-1-4613-6624-9Springer Science+Business Media New York 1989

作者: coagulation 時間: 2025-3-25 18:51

作者: 不如樂死去 時間: 2025-3-25 22:17

作者: Misnomer 時間: 2025-3-26 01:14

作者: 食品室 時間: 2025-3-26 06:54

作者: debacle 時間: 2025-3-26 12:02
https://doi.org/10.1007/978-1-84628-915-6hapters, we have used phones as the fundamental unit of speech. An even more natural unit is words. In this chapter, we will discuss the strengths and 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 subst

作者: Flinch 時間: 2025-3-26 12:55

作者: Isometric 時間: 2025-3-26 19:29
Changes for Global Products and PLM,ov modeling, a powerful mathematical learning paradigm. We also decided to use vector quantization and discrete HMMs for expedience and practicality. Then we attacked the problems of large vocabulary, speaker independence, and continuous speech within our discrete HMM framework.

作者: 使更活躍 時間: 2025-3-27 00:41

作者: 有危險 時間: 2025-3-27 04:22
Hidden Markov Modeling of Speech,ognition independently at CMU [Baker 75a] and IBM [Bakis 76, Jelinek 76]. It was only in the past few years, however, that HMMs became the predominant approach to speech recognition, superseding dynamic time warping.

作者: 線 時間: 2025-3-27 07:27