標題: Titlebook: Automatic Speech and Speaker Recognition; Advanced Topics Chin-Hui Lee,Frank K. Soong,Kuldip K. Paliwal Book 1996 Kluwer Academic Publisher [打印本頁] 作者: 倒鉤 時間: 2025-3-21 18:08
書目名稱Automatic Speech and Speaker Recognition影響因子(影響力)
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書目名稱Automatic Speech and Speaker Recognition讀者反饋
書目名稱Automatic Speech and Speaker Recognition讀者反饋學科排名
作者: Nibble 時間: 2025-3-21 23:53
An Overview of Speaker Recognition Technology,ecognition can be divided in two ways: (a) speaker identification and verification, and (b) text-dependent and text-independent methods. The second part of the paper is devoted to discussion of more specific topics of recent interest which have led to interesting new approaches and techniques. They 作者: aspect 時間: 2025-3-22 02:23 作者: 正常 時間: 2025-3-22 06:54
Bayesian Adaptive Learning and Map Estimation of HMM,orithms are then developed for hidden Markov models and for a number of useful parametric densities commonly used in automatic speech recognition and natural language processing. The MAP formulation offers a way to combine existing prior knowledge and a small set of newly acquired task-specific data作者: Mucosa 時間: 2025-3-22 11:06
Statistical and Discriminative Methods for Speech Recognition,erns or models for accurate pattern comparison. In this chapter, we discuss the issue of speech recognizer training from a broad perspective with root in the classical Bayes decision theory. We differentiate the method of classifier design by way of distribution estimation and the method of discrimi作者: 要控制 時間: 2025-3-22 14:31 作者: 來就得意 時間: 2025-3-22 19:41 作者: venous-leak 時間: 2025-3-23 00:13 作者: 悠然 時間: 2025-3-23 04:35
Voice Identification Using Nonparametric Density Matching,characterized by stable, speaker-unique probability density functions (PDFs). This chapter describes a method of comparing speech utterances to determine whether or not the underlying PDFs are the same, hence likely to have been spoken by the same person. The method is independent of assumptions abo作者: chandel 時間: 2025-3-23 05:44 作者: 洞察力 時間: 2025-3-23 09:54
Hybrid Connectionist Models For Continuous Speech Recognition,nd flexible, but the probability estimation techniques used with these models typically suffer from a number of significant limitations. Over the last few years, we have demonstrated that fairly simple Multi-Layered Perceptrons (MLPs) can be discriminatively trained to estimate emission probabilitie作者: Arb853 時間: 2025-3-23 16:09
Automatic Generation of Detailed Pronunciation Lexicons,the source of sub-words units for which we build acoustic models (1) a coarse phonemic representation, (2) a single, fine phonetic realization, and (3) multiple phonetic realizations with associated likelihoods. We describe how we obtain these different pronunciations from text-to-speech systems and作者: absolve 時間: 2025-3-23 21:26 作者: 五行打油詩 時間: 2025-3-24 01:30 作者: Acupressure 時間: 2025-3-24 06:20 作者: panorama 時間: 2025-3-24 09:21
Dynamic Programming Search Strategies: From Digit Strings to Large Vocabulary Word Graphs,basic one-pass algorithm for word string recognition, we extend the search strategy to vocabularies of 20,000 words and more by using a tree organization of the vocabulary. Finally, we describe how this predecessor conditioned algorithm can be refined to produce high-quality word graphs. This method作者: 極小 時間: 2025-3-24 13:09
Multiple-Pass Search Strategies,ially, without any increase in error rate. We consider two basic strategies: the N-best Paradigm, and the Forward-Backward search. Both of these strategies operate on the entire sentence in (at least) two passes. The N-best Paradigm computes alternative hypotheses for a sentence, which can later be 作者: 傲慢物 時間: 2025-3-24 17:10 作者: 懲罰 時間: 2025-3-24 22:47 作者: Anemia 時間: 2025-3-25 00:02 作者: GLUE 時間: 2025-3-25 05:20 作者: 表主動 時間: 2025-3-25 09:39 作者: START 時間: 2025-3-25 13:52 作者: 保全 時間: 2025-3-25 16:32 作者: Accede 時間: 2025-3-25 23:16
https://doi.org/10.1007/978-3-319-72529-1erns or models for accurate pattern comparison. In this chapter, we discuss the issue of speech recognizer training from a broad perspective with root in the classical Bayes decision theory. We differentiate the method of classifier design by way of distribution estimation and the method of discrimi作者: Amenable 時間: 2025-3-26 02:31
https://doi.org/10.1007/978-3-319-72529-1he relevant feature space(s). This is especially true in continuous speech and/or for speaker-independent tasks, where pronunciation variability is the greatest. In this chapter, we will discuss a number of clustering techniques which can be used to derive high quality acoustic prototypes.作者: cognizant 時間: 2025-3-26 05:17 作者: 真實的你 時間: 2025-3-26 10:20 作者: PIZZA 時間: 2025-3-26 14:24 作者: 雪上輕舟飛過 時間: 2025-3-26 17:41 作者: Dislocation 時間: 2025-3-27 00:01 作者: PALL 時間: 2025-3-27 03:10 作者: subordinate 時間: 2025-3-27 05:59 作者: 凝結劑 時間: 2025-3-27 12:07 作者: 注意 時間: 2025-3-27 16:05
Warehousing: Improving Customer Servicecessing to achieve robust speech recognition, discussing and comparing approaches based on direct cepstral comparisons, on parametric models of environmental degradation, and on cepstral high-pass filtering. We also describe and compare the effectiveness of two complementary methods of signal proces作者: Surgeon 時間: 2025-3-27 20:36 作者: Pde5-Inhibitors 時間: 2025-3-28 01:39 作者: Volatile-Oils 時間: 2025-3-28 03:36
Hardware: RFID Tags and Readersthe form of the IBM Personal Dictation System required substantial innovation in all areas of speech recognition, from signal processing to language modeling. This chapter describes some of the algorithmic techniques that had to be developed in order to create a dictation system that could actually 作者: 共同確定為確 時間: 2025-3-28 07:45 作者: 首創(chuàng)精神 時間: 2025-3-28 13:58 作者: 移動 時間: 2025-3-28 16:09 作者: 愛社交 時間: 2025-3-28 19:08 作者: 無動于衷 時間: 2025-3-28 23:53 作者: 朋黨派系 時間: 2025-3-29 06:55 作者: Bravura 時間: 2025-3-29 09:51 作者: 改革運動 時間: 2025-3-29 12:13
Hardware: RFID Tags and Readersthe form of the IBM Personal Dictation System required substantial innovation in all areas of speech recognition, from signal processing to language modeling. This chapter describes some of the algorithmic techniques that had to be developed in order to create a dictation system that could actually be used by real users to create text.作者: exceed 時間: 2025-3-29 15:58 作者: 浪費時間 時間: 2025-3-29 21:03
Statistical and Discriminative Methods for Speech Recognition, methods in the context of hidden Markov modeling for speech recognition. We show the superiority of the discriminative method over the distribution estimation method by providing the results of several key speech recognition experiments.作者: 乳白光 時間: 2025-3-30 02:26 作者: outset 時間: 2025-3-30 07:22 作者: GIBE 時間: 2025-3-30 08:49 作者: Sedative 時間: 2025-3-30 15:12 作者: Lime石灰 時間: 2025-3-30 19:17
Outlook: Navigating the Sea of Datas for HMMs. Simple context-independent systems based on this approach have performed very well on large vocabulary continuous speech recognition. This chapter will briefly review the fundamentals of HMMs and MLPs, and will then describe a form of hybrid system that has some discriminant properties.作者: Bother 時間: 2025-3-30 20:46 作者: faculty 時間: 2025-3-31 03:19 作者: Mammal 時間: 2025-3-31 05:46 作者: 摻和 時間: 2025-3-31 09:27 作者: 太空 時間: 2025-3-31 13:53
Infrastructure: EPCglobal Network decoding). The advantages of using recurrent networks are that they require a small number of parameters and provide a fast decoding capability (relative to conventional, large-vocabulary, HMM systems)..作者: Plaque 時間: 2025-3-31 18:53
An Overview of Automatic Speech Recognition,vances in several areas of automatic speech recognition. We also briefly discuss the requirements in designing successful real-world applications and address technical challenges that need to be faced in order to reach the ultimate goal of providing an easy-to-use, natural, and flexible voice interface between people and machines.作者: 金桌活畫面 時間: 2025-3-31 22:05 作者: 遵循的規(guī)范 時間: 2025-4-1 03:19
The Use of Recurrent Neural Networks in Continuous Speech Recognition, decoding). The advantages of using recurrent networks are that they require a small number of parameters and provide a fast decoding capability (relative to conventional, large-vocabulary, HMM systems)..作者: jaundiced 時間: 2025-4-1 07:13
An Overview of Speaker Recognition Technology,include parameter/distance normalization techniques, model adaptation techniques, VQ-/ergodic-HMM-based text-independent recognition methods, and a text-prompted recognition method. The chapter concludes with a short discussion assessing the current status and possibilities for the future.作者: 常到 時間: 2025-4-1 13:14
