標(biāo)題: Titlebook: Computational Models of Speech Pattern Processing; Keith Ponting Book 1999 Springer-Verlag Berlin Heidelberg 1999 Dialogue systems.Human s [打印本頁] 作者: 強(qiáng)烈的愿望 時(shí)間: 2025-3-21 19:22
書目名稱Computational Models of Speech Pattern Processing影響因子(影響力)
書目名稱Computational Models of Speech Pattern Processing影響因子(影響力)學(xué)科排名
書目名稱Computational Models of Speech Pattern Processing網(wǎng)絡(luò)公開度
書目名稱Computational Models of Speech Pattern Processing網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computational Models of Speech Pattern Processing被引頻次
書目名稱Computational Models of Speech Pattern Processing被引頻次學(xué)科排名
書目名稱Computational Models of Speech Pattern Processing年度引用
書目名稱Computational Models of Speech Pattern Processing年度引用學(xué)科排名
書目名稱Computational Models of Speech Pattern Processing讀者反饋
書目名稱Computational Models of Speech Pattern Processing讀者反饋學(xué)科排名
作者: 符合規(guī)定 時(shí)間: 2025-3-21 23:28
978-3-642-64250-0Springer-Verlag Berlin Heidelberg 1999作者: mitral-valve 時(shí)間: 2025-3-22 01:54
Speaker Adaptation of CDHMMs Using Bayesian Learning,he parameters of the Gaussian mixture output densities are adapted using the exponential forgetting mechanism and performing the . parameter estimation in a model based outline. Moreover a channel adaptation is carried out by means of the cepstral mean normalization method (CMN).作者: EVICT 時(shí)間: 2025-3-22 07:25 作者: Vulvodynia 時(shí)間: 2025-3-22 12:18 作者: NEXUS 時(shí)間: 2025-3-22 15:41 作者: NEXUS 時(shí)間: 2025-3-22 17:23 作者: 柔軟 時(shí)間: 2025-3-22 21:43
NATO ASI Subseries F:http://image.papertrans.cn/c/image/232845.jpg作者: Abrade 時(shí)間: 2025-3-23 02:49 作者: bronchodilator 時(shí)間: 2025-3-23 09:34 作者: rheumatology 時(shí)間: 2025-3-23 11:50
Quantenchemie auf Workstation-Clustern still have to be enhanced, especially to improve the robustness of recognition in real conditions. Most of present systems are based on stochastic models, especially hidden Markov models (HMMs). In the past few years, a quite large number of projects have been directed toward the development of a n作者: 小樣他閑聊 時(shí)間: 2025-3-23 15:01 作者: 半圓鑿 時(shí)間: 2025-3-23 21:39 作者: Glucose 時(shí)間: 2025-3-24 02:05
Fujitsu VP2000 Series Supercomputer,ecognizer performance. Recently, the . (DFE) method has been applied for estimating transformations of the representation space for speech recognizers. In this work, a variant of the DFE method is applied in order to improve the representation space for Continuous Speech Recognition.作者: indices 時(shí)間: 2025-3-24 04:01 作者: Conspiracy 時(shí)間: 2025-3-24 07:21
Science, Simulation and Supercomputersment of fast K-NN algorithms allows to reconsider its use in applications with large sample sets. In this outlook, the K-NN decision principle has been assessed on a frame by frame phonetic identification on the TIMIT database. Thereafter, a method to integrate the K-NN pdf estimator in a HMM-based 作者: 斜谷 時(shí)間: 2025-3-24 14:30 作者: GUILT 時(shí)間: 2025-3-24 15:57
An Overview of Computing at Los Alamoses have been proposed for resolving this mismatch problem. These approaches can be divided broadly into three classes: model adaptation, channel adaptation and robust features. This paper presents a review and discussion of methods for channel adaptation and their relationship to methods in the othe作者: microscopic 時(shí)間: 2025-3-24 19:11 作者: 協(xié)迫 時(shí)間: 2025-3-25 00:28 作者: Cpap155 時(shí)間: 2025-3-25 03:21
https://doi.org/10.1007/978-3-030-36592-9sed recognizer. This is achieved by using a phonetic classifier during the training phase. Three broad phonetic classes: voiced frames, unvoiced frames and transitions, are defined. We design speaker templates by the combination of four single state HMMs into a four state HMM after re-estimation of 作者: 粗魯性質(zhì) 時(shí)間: 2025-3-25 10:36 作者: facilitate 時(shí)間: 2025-3-25 15:41
https://doi.org/10.1007/978-3-030-36592-9se models, which can be broadly classified as segment models, are surveyed in this chapter and presented in a general probabilistic framework that includes the hidden Markov model (HMM) as a special case. The overview gives options for modeling assumptions in terms of correlation structure and param作者: 可能性 時(shí)間: 2025-3-25 16:51
Supercomputing Facilities for the 1990sisms of speech production than the typical mel-cepstrum representation. Initial developments are described towards using linear dynamic segmental HMMs [12] to model underlying (unobserved) trajectories of features which closely reflect the nature of articulation. So far, this work has involved calcu作者: Analogy 時(shí)間: 2025-3-25 19:59 作者: 極深 時(shí)間: 2025-3-26 02:41
Computational Models of Speech Pattern Processing978-3-642-60087-6Series ISSN 0258-1248 作者: Ostrich 時(shí)間: 2025-3-26 06:12 作者: 小歌劇 時(shí)間: 2025-3-26 12:12 作者: Musculoskeletal 時(shí)間: 2025-3-26 14:29
Fujitsu VP2000 Series Supercomputer,ecognizer performance. Recently, the . (DFE) method has been applied for estimating transformations of the representation space for speech recognizers. In this work, a variant of the DFE method is applied in order to improve the representation space for Continuous Speech Recognition.作者: 和音 時(shí)間: 2025-3-26 17:18
https://doi.org/10.1007/978-1-4684-5021-7ions. These technologies are reviewed from the viewpoint of a stochastic pattern matching paradigm for speech recognition. Improved robustness enables better speech recognition over a wide range of unexpected and adverse conditions by reducing mismatches between training and testing speech utterances.作者: 羽毛長成 時(shí)間: 2025-3-27 00:10
An Overview of Computing at Los Alamoses have been proposed for resolving this mismatch problem. These approaches can be divided broadly into three classes: model adaptation, channel adaptation and robust features. This paper presents a review and discussion of methods for channel adaptation and their relationship to methods in the other classes.作者: cultivated 時(shí)間: 2025-3-27 03:38 作者: neurologist 時(shí)間: 2025-3-27 09:18 作者: relieve 時(shí)間: 2025-3-27 12:56 作者: 鬼魂 時(shí)間: 2025-3-27 14:20
Trajectory Representations and Acoustic Descriptions for a Segment-Modelling Approach to Automatic lating segment probabilities using an approach which is different from that used in earlier studies (e.g. [4]), and is more consistent with the idea of treating the trajectory as unobserved. In parallel, experiments have demonstrated that formant features can be useful for HMM-based automatic speech recognition [3].作者: 推測(cè) 時(shí)間: 2025-3-27 20:54 作者: ostrish 時(shí)間: 2025-3-27 22:26
D. Barkai,K. J. M. Moriarty,C. Rebbispeech quality by adding natural characteristics of voice individuality, and converting synthesized voice individuality from one speaker to another, are as yet little exploited research fields to be studied in the near future.作者: Offensive 時(shí)間: 2025-3-28 05:07 作者: Alienated 時(shí)間: 2025-3-28 10:01
Supercomputing Facilities for the 1990slating segment probabilities using an approach which is different from that used in earlier studies (e.g. [4]), and is more consistent with the idea of treating the trajectory as unobserved. In parallel, experiments have demonstrated that formant features can be useful for HMM-based automatic speech recognition [3].作者: 思考 時(shí)間: 2025-3-28 14:25 作者: 災(zāi)禍 時(shí)間: 2025-3-28 14:34 作者: Scintigraphy 時(shí)間: 2025-3-28 20:36 作者: muster 時(shí)間: 2025-3-29 02:17
Supercomputing: Key Issues and Challengesistic hypotheses. So far, the most useful prosodic information is provided by clause boundaries. These are detected with a recognition rate of 94%. For the parsing of word hypotheses graphs, the use of clause boundary probabilities yields a speed-up of 92% and a 96% reduction of alternative readings.作者: macular-edema 時(shí)間: 2025-3-29 05:21 作者: Parameter 時(shí)間: 2025-3-29 09:12
Acoustic Modelling for Large Vocabulary Continuous Speech Recognition,tation allows a set of HMM parameter transforms to be robustly estimated using small amounts of adaptation data. Secondly, MMI training based on lattices can be used to increase the inherent discrimination of the HMMs.作者: indubitable 時(shí)間: 2025-3-29 11:31 作者: Ambulatory 時(shí)間: 2025-3-29 17:35 作者: Forehead-Lift 時(shí)間: 2025-3-29 21:02
Science, Simulation and Supercomputersn assessed on a frame by frame phonetic identification on the TIMIT database. Thereafter, a method to integrate the K-NN pdf estimator in a HMM-based system is proposed and tested on an acoustic-phonetic decoding task.作者: FOR 時(shí)間: 2025-3-30 03:52 作者: 沒有貧窮 時(shí)間: 2025-3-30 05:14 作者: Ejaculate 時(shí)間: 2025-3-30 10:37
https://doi.org/10.1007/978-3-030-36592-9ludes the hidden Markov model (HMM) as a special case. The overview gives options for modeling assumptions in terms of correlation structure and parameter tying and outlines the extensions to HMM recognition and training algorithms needed to handle segment models.作者: thwart 時(shí)間: 2025-3-30 13:15
K-Nearest Neighbours Estimator in a HMM-Based Recognition System,n assessed on a frame by frame phonetic identification on the TIMIT database. Thereafter, a method to integrate the K-NN pdf estimator in a HMM-based system is proposed and tested on an acoustic-phonetic decoding task.作者: 石墨 時(shí)間: 2025-3-30 18:37
Application of Acoustic Discriminative Training in an Ergodic HMM for Speaker Identification,s and transitions, are defined. We design speaker templates by the combination of four single state HMMs into a four state HMM after re-estimation of the transition probabilities. Experiments conducted with two databases are reported, and the results show that this architecture performs better than others without phonetic classification.作者: Mettle 時(shí)間: 2025-3-30 22:18
Comparison of Several Compensation Techniques for Robust Speaker Verification,derably. In this paper, different techniques which make a speaker verification system more robust against noise are described and compared. Some of these techniques have already been successfully applied in Robust Speech Recognition, and our preliminary results show that they are also very encouraging for Robust Speaker Verification.作者: invert 時(shí)間: 2025-3-31 01:58 作者: Optometrist 時(shí)間: 2025-3-31 07:26 作者: 挫敗 時(shí)間: 2025-3-31 12:12 作者: Immunoglobulin 時(shí)間: 2025-3-31 14:29
