標(biāo)題: Titlebook: Deep Learning Approaches for Spoken and Natural Language Processing; Virender Kadyan,Amitoj Singh,Laith Abualigah Book 2021 The Editor(s) [打印本頁] 作者: HAG 時間: 2025-3-21 19:55
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書目名稱Deep Learning Approaches for Spoken and Natural Language Processing被引頻次
書目名稱Deep Learning Approaches for Spoken and Natural Language Processing被引頻次學(xué)科排名
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書目名稱Deep Learning Approaches for Spoken and Natural Language Processing讀者反饋
書目名稱Deep Learning Approaches for Spoken and Natural Language Processing讀者反饋學(xué)科排名
作者: 主動 時間: 2025-3-21 20:30
Signals and Communication Technologyhttp://image.papertrans.cn/d/image/264570.jpg作者: 命令變成大炮 時間: 2025-3-22 01:45
Deep Learning Approaches for Spoken and Natural Language Processing978-3-030-79778-2Series ISSN 1860-4862 Series E-ISSN 1860-4870 作者: Expiration 時間: 2025-3-22 04:49
https://doi.org/10.1007/978-3-030-79778-2Handwriting Recognition; Pattern Recognition; Speech Recognition; Spoken Language Processing; Writer Ide作者: 依法逮捕 時間: 2025-3-22 08:44 作者: 鞭打 時間: 2025-3-22 15:03 作者: 鞭打 時間: 2025-3-22 18:27
Xiao Guo,Zhenjiang Shen,Xiao Teng,Yong Linmultiple choice and true/false questions because the answers are specific compared with essay question answers. Automatic grading system (AGS) was developed to evaluate essay answers using a computer program that solves manual grading process problems like high cost, time-consuming task, increasing 作者: Affluence 時間: 2025-3-22 21:36
Junjie Li,Shuo Tian,Yuanhui Liu corresponding to every vowel for the improved efficiency of Automatic Speech Recognition (ASR) system. In this research, the linguistic study of native speakers and their auditory inconsistency was pursued using the extraction of efficient front-end speech vectors utilizing three varying fractal di作者: 補助 時間: 2025-3-23 03:55 作者: 哄騙 時間: 2025-3-23 09:01 作者: COLIC 時間: 2025-3-23 12:14 作者: LARK 時間: 2025-3-23 16:13
Design and Testing of Reversible Logicd as a potential human behavioral trait to cope with various smart systems. Influence of speech-driven devices, whether it is speech recognition based or speaker recognition based, can be marked at various places in today’s life. This chapter discusses the private and public sources of speech data t作者: 類人猿 時間: 2025-3-23 21:12 作者: CAGE 時間: 2025-3-24 01:38
Design and Testing of Reversible Logic trend. In the past few decades, researchers have focused on integrating ensemble learning methods alongside the use of semi-supervised learning paradigm to construct more detailed and efficient classification systems. Likewise, male and female anatomical differences in human speech are related to t作者: TRACE 時間: 2025-3-24 02:38 作者: 嚴(yán)重傷害 時間: 2025-3-24 09:19 作者: Grasping 時間: 2025-3-24 12:46
Optimal Fractal Feature Selection and Estimation for Speech Recognition Under Mismatched ConditionsC) have been recorded with modest changes using hidden Markov models (HMM). The selection of optimal features was made possible by increasing child data through adaptation measures on adult data, which has allowed for the examination of new features under mismatched conditions resulting in an overal作者: LEVER 時間: 2025-3-24 15:23 作者: geriatrician 時間: 2025-3-24 21:01
Classical and Deep Learning Data Processing Techniques for Speech and Speaker Recognitions,ion technique. Analysis of this chapter indicates that classical feature extraction techniques of cepstral domain like Mel Frequency Cepstral Coefficients (MFCC) are the most popular and better in performance for speech and speaker recognition tasks. This chapter provides the implementation details 作者: 骨 時間: 2025-3-25 02:25 作者: Forsake 時間: 2025-3-25 05:53
Noise-Robust Gender Classification System Through Optimal Selection of Acoustic Features,resence of loud backgrounds as well as their evaluations and possible impacts on practical efficiency. Finally, three semi-supervised classification algorithms including random forest, support vector machine (SVM), and multi-layer perceptron (MLP) have been experimented resulting in the increased pe作者: BROTH 時間: 2025-3-25 09:26
https://doi.org/10.1007/978-3-642-70926-5have become interested in analyzing people’s feelings through social networks, especially in political and economic domains. The main task is to classify the level of messages or tweets to their polarity. In this research, we will look at the most important approaches used in sentiment analysis and 作者: Endemic 時間: 2025-3-25 14:07 作者: Lasting 時間: 2025-3-25 19:16
Junjie Li,Shuo Tian,Yuanhui LiuC) have been recorded with modest changes using hidden Markov models (HMM). The selection of optimal features was made possible by increasing child data through adaptation measures on adult data, which has allowed for the examination of new features under mismatched conditions resulting in an overal作者: 易于 時間: 2025-3-25 20:02 作者: CESS 時間: 2025-3-26 01:45
Design and Testing of Reversible Logicion technique. Analysis of this chapter indicates that classical feature extraction techniques of cepstral domain like Mel Frequency Cepstral Coefficients (MFCC) are the most popular and better in performance for speech and speaker recognition tasks. This chapter provides the implementation details 作者: legitimate 時間: 2025-3-26 08:04 作者: 有雜色 時間: 2025-3-26 12:16 作者: 6Applepolish 時間: 2025-3-26 16:19
Book 2021 techniques can be applied to improve NLP and speech processing applications;.Presents and escalates the research trends and future direction of language and speech processing;.Includes theoretical research, experimental results, and applications of deep learning..作者: anchor 時間: 2025-3-26 19:46
1860-4862 escalates the research trends and future direction of language and speech processing;.Includes theoretical research, experimental results, and applications of deep learning..978-3-030-79780-5978-3-030-79778-2Series ISSN 1860-4862 Series E-ISSN 1860-4870 作者: instructive 時間: 2025-3-27 01:00 作者: arthroplasty 時間: 2025-3-27 02:24 作者: Conspiracy 時間: 2025-3-27 07:25 作者: 后天習(xí)得 時間: 2025-3-27 09:41
Optimal Fractal Feature Selection and Estimation for Speech Recognition Under Mismatched Conditions corresponding to every vowel for the improved efficiency of Automatic Speech Recognition (ASR) system. In this research, the linguistic study of native speakers and their auditory inconsistency was pursued using the extraction of efficient front-end speech vectors utilizing three varying fractal di作者: NOT 時間: 2025-3-27 15:17
Class Diagram Generation from Text Requirements: An Application of Natural Language Processing,equirements to sketching the program’s design, which is an essential task for programmers and software engineers. The motivation behind this task is related to the designations of software requirements created in the natural language (NL). To minimize these errors, we can transfer the software requi作者: PLE 時間: 2025-3-27 18:38
Semantic Similarity and Paraphrase Identification for Malayalam Using Deep Autoencoders,he unsupervised learning of phrase representations to extract features for paraphrase identification. Sentence’s features of varying lengths are converted to fixed-size representation using the convolution method of dynamic pooling. Initially, the Malayalam paraphrase identification system was desig作者: 賄賂 時間: 2025-3-27 23:01 作者: 耐寒 時間: 2025-3-28 05:13 作者: 催眠 時間: 2025-3-28 08:38
Automatic Speech Recognition in English Language: A Review,re present on the market. Speech recognition applications are becoming useful and popular where typing was a challenging job. As the demands for new emergency medical services (EMS) are increasing, speech recognition systems (SRS) are easily accessible and may fulfill this demand. In addition, SRS m作者: 克制 時間: 2025-3-28 11:50
Noise-Robust Gender Classification System Through Optimal Selection of Acoustic Features, trend. In the past few decades, researchers have focused on integrating ensemble learning methods alongside the use of semi-supervised learning paradigm to construct more detailed and efficient classification systems. Likewise, male and female anatomical differences in human speech are related to t作者: 陶器 時間: 2025-3-28 15:02
1860-4862 cessing applications.Presents and escalates the research treThis book provides insights into how deep learning techniques impact language and speech processing applications. The authors discuss the promise, limits and the new challenges in deep learning. The book covers the major differences between作者: Lobotomy 時間: 2025-3-28 20:35 作者: CHOKE 時間: 2025-3-29 00:26 作者: NUDGE 時間: 2025-3-29 04:50 作者: Keratectomy 時間: 2025-3-29 08:06
Book 2021its and the new challenges in deep learning. The book covers the major differences between the various applications of deep learning and the classical machine learning techniques. The main objective of the book is to present a comprehensive survey of the major applications and research oriented arti
