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Artículos de revistas sobre el tema "Automatic speech recognition"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

Fried, Louis. "AUTOMATIC SPEECH RECOGNITION." Information Systems Management 13, no. 1 (1996): 29–37. http://dx.doi.org/10.1080/10580539608906969.

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2

Chigier, Benjamin. "Automatic speech recognition." Journal of the Acoustical Society of America 103, no. 1 (1998): 19. http://dx.doi.org/10.1121/1.423151.

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3

Hovell, Simon Alexander. "Automatic speech recognition." Journal of the Acoustical Society of America 107, no. 5 (2000): 2325. http://dx.doi.org/10.1121/1.428610.

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4

Espy‐Wilson, Carol. "Automatic speech recognition." Journal of the Acoustical Society of America 117, no. 4 (2005): 2403. http://dx.doi.org/10.1121/1.4786105.

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5

Merrill, John W. "Automatic speech recognition." Journal of the Acoustical Society of America 121, no. 1 (2007): 29. http://dx.doi.org/10.1121/1.2434334.

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6

Rao, P. V. S., and K. K. Paliwal. "Automatic speech recognition." Sadhana 9, no. 2 (1986): 85–120. http://dx.doi.org/10.1007/bf02747521.

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7

SAYEM, Asm. "Speech Analysis for Alphabets in Bangla Language: Automatic Speech Recognition." International Journal of Engineering Research 3, no. 2 (2014): 88–93. http://dx.doi.org/10.17950/ijer/v3s2/211.

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8

Carlson, Gloria Stevens, and Jared Bernstein. "Automatic speech recognition of impaired speech." International Journal of Rehabilitation Research 11, no. 4 (1988): 396–97. http://dx.doi.org/10.1097/00004356-198812000-00013.

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9

SAGISAKA, Yoshinori. "AUTOMATIC SPEECH RECOGNITION MODELS." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 22, no. 1 (1995): 40–47. http://dx.doi.org/10.2333/jbhmk.22.40.

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10

Receveur, Simon, Robin Weiss, and Tim Fingscheidt. "Turbo Automatic Speech Recognition." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, no. 5 (2016): 846–62. http://dx.doi.org/10.1109/taslp.2016.2520364.

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11

Dutta Majumder, D. "Fuzzy sets in pattern recognition, image analysis and automatic speech recognition." Applications of Mathematics 30, no. 4 (1985): 237–54. http://dx.doi.org/10.21136/am.1985.104148.

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12

Carlson, Gloria Stevens, Jared Bernstein, and Donald W. Bell. "Automatic speech recognition for speech‐impaired people." Journal of the Acoustical Society of America 84, S1 (1988): S46. http://dx.doi.org/10.1121/1.2026325.

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13

King, Simon, Joe Frankel, Karen Livescu, Erik McDermott, Korin Richmond, and Mirjam Wester. "Speech production knowledge in automatic speech recognition." Journal of the Acoustical Society of America 121, no. 2 (2007): 723–42. http://dx.doi.org/10.1121/1.2404622.

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14

Lou, Lingling, Wei Xu, Ruijia Liu, and Zhonggen Yu. "A Bibliometric Approach and Meta-Analysis of Effects of Automatic Speech Recognition on Second Language Learning." International Journal of Web-Based Learning and Teaching Technologies 19, no. 1 (2024): 1–20. http://dx.doi.org/10.4018/ijwltt.349959.

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Although automatic speech recognition technologies have gained popularity in second language learning, there are still difficulties facing the use of automatic speech recognition in second language learning. Through a bibliometric review and a meta-analysis, this study examines the effects of automatic speech recognition on second language learning. The results of this investigation show that automatic speech recognition technologies can improve second language speaking skills and reduce speaking anxiety, but whether the listening skills can be improved relies on the degree of advancement of t

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15

Sar, Leda, Mark Hasegawa-Johnson, and Chang D. Yoo. "Counterfactually Fair Automatic Speech Recognition." IEEE/ACM Transactions on Audio, Speech, and Language Processing 29 (2021): 3515–25. http://dx.doi.org/10.1109/taslp.2021.3126949.

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16

J.Arora, Shipra, and Rishi Pal Singh. "Automatic Speech Recognition: A Review." International Journal of Computer Applications 60, no. 9 (2012): 34–44. http://dx.doi.org/10.5120/9722-4190.

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17

Cooke, N. J., and M. Russell. "Gaze-contingent automatic speech recognition." IET Signal Processing 2, no. 4 (2008): 369. http://dx.doi.org/10.1049/iet-spr:20070127.

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18

Francart, Tom, Marc Moonen, and Jan Wouters. "Automatic testing of speech recognition." International Journal of Audiology 48, no. 2 (2009): 80–90. http://dx.doi.org/10.1080/14992020802400662.

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19

Vensko, George. "Apparatus for automatic speech recognition." Journal of the Acoustical Society of America 85, no. 4 (1989): 1812. http://dx.doi.org/10.1121/1.397922.

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20

JONES, DYLAN M., CLIVE R. FRANKISH, and KEVIN HAPESHI. "Automatic speech recognition in practice." Behaviour & Information Technology 11, no. 2 (1992): 109–22. http://dx.doi.org/10.1080/01449299208924325.

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21

Espy-Wilson, Carol Y. "Linguistically informed automatic speech recognition." Journal of the Acoustical Society of America 112, no. 5 (2002): 2278. http://dx.doi.org/10.1121/1.1526574.

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22

Cerisara, Christophe, and Dominique Fohr. "Multi-band automatic speech recognition." Computer Speech & Language 15, no. 2 (2001): 151–74. http://dx.doi.org/10.1006/csla.2001.0163.

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23

Singhal, Shiwani, Muskan Deswal, and Er Shafalii Sharma. "AUTOMATIC SPEECH RECOGNITION USING DEEP NEURAL NETWORKS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27313.

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This research concentrates on the progression and present status of automatic speech recognition systems powered by deep neural networks. It discusses model architectures, training approaches, evaluating model efficacy, and recent advancements specific to deep neural networks applied in automatic speech recognition models. It considers the challenges faced in crafting these speech recognition models, such as data scarcity and the necessity for adaptability. Our exploration traces the evolution of automatic speech recognition through deep neural networks, presenting valuable insights aimed at p

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24

Namita Kure, Et al. "Survey of Automatic Dysarthric Speech Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1028–36. http://dx.doi.org/10.17762/ijritcc.v11i10.8622.

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The need for automated speech recognition has expanded as a result of significant industrial expansion for a variety of automation and human-machine interface applications. The speech impairment brought on by communication disorders, neurogenic speech disorders, or psychological speech disorders limits the performance of different artificial intelligence-based systems. The dysarthric condition is a neurogenic speech disease that restricts the capacity of the human voice to articulate. This article presents a comprehensive survey of the recent advances in the automatic Dysarthric Speech Recogni

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25

Kushwaha, Shivam, Piyush Deep, Mohd Muaz, and Er Shafalii Sharma. "AUTOMATIC SPEECH RECOGNITION USING DEEP NEURAL NETWORKS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27292.

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This research paper revolves around the evolution and the present scenario of Automatic Speech Recognition systems using Deep Neural Networks. It includes the designs, techniques of training, the evaluations of the model performance, and the emerging trends that are specific to deep neural networks embedded in automatic speech recognition models. This research also incorporates the challenges faced while building and deploying the speech recognition model including limited data availability and adaptability. We have examined how deep neural networks have transformed automatic speech recognitio

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26

Kewley-Port, Diane, Jonathan Dalby, and Deborah Burleson. "Speech intelligibility training using automatic speech recognition technology." Journal of the Acoustical Society of America 112, no. 5 (2002): 2303–4. http://dx.doi.org/10.1121/1.4779274.

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27

Benzeghiba, M., R. De Mori, O. Deroo, et al. "Automatic speech recognition and speech variability: A review." Speech Communication 49, no. 10-11 (2007): 763–86. http://dx.doi.org/10.1016/j.specom.2007.02.006.

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28

Salaja, Rosemary T., Ronan Flynn, and Michael Russell. "A Life-Based Classifier for Automatic Speech Recognition." Applied Mechanics and Materials 679 (October 2014): 189–93. http://dx.doi.org/10.4028/www.scientific.net/amm.679.189.

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Research in speech recognition has produced different approaches that have been used for the classification of speech utterances in the back-end of an automatic speech recognition (ASR) system. As speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. This paper proposes a new back-end classifier that is based on artificial life (ALife) and describes how the proposed classifier can be used in a speech recognition system.

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29

Janai, Siddhanna, Shreekanth T., Chandan M., and Ajish K. Abraham. "Speech-to-Speech Conversion." International Journal of Ambient Computing and Intelligence 12, no. 1 (2021): 184–206. http://dx.doi.org/10.4018/ijaci.2021010108.

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A novel approach to build a speech-to-speech conversion (STSC) system for individuals with speech impairment dysarthria is described. STSC system takes impaired speech having inherent disturbance as input and produces a synthesized output speech with good pronunciation and noise free utterance. The STSC system involves two stages, namely automatic speech recognition (ASR) and automatic speech synthesis. ASR transforms speech into text, while automatic speech synthesis (or text-to-speech [TTS]) performs the reverse task. At present, the recognition system is developed for a small vocabulary of

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30

Schultz, Benjamin G., Venkata S. Aditya Tarigoppula, Gustavo Noffs, et al. "Automatic speech recognition in neurodegenerative disease." International Journal of Speech Technology 24, no. 3 (2021): 771–79. http://dx.doi.org/10.1007/s10772-021-09836-w.

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AbstractAutomatic speech recognition (ASR) could potentially improve communication by providing transcriptions of speech in real time. ASR is particularly useful for people with progressive disorders that lead to reduced speech intelligibility or difficulties performing motor tasks. ASR services are usually trained on healthy speech and may not be optimized for impaired speech, creating a barrier for accessing augmented assistance devices. We tested the performance of three state-of-the-art ASR platforms on two groups of people with neurodegenerative disease and healthy controls. We further ex

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31

Auti, Dr Nisha, Atharva Pujari, Anagha Desai, Shreya Patil, Sanika Kshirsagar, and Rutika Rindhe. "Advanced Audio Signal Processing for Speaker Recognition and Sentiment Analysis." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1717–24. http://dx.doi.org/10.22214/ijraset.2023.51825.

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Abstract: Automatic Speech Recognition (ASR) technology has revolutionized human-computer interaction by allowing users to communicate with computer interfaces using their voice in a natural way. Speaker recognition is a biometric recognition method that identifies individuals based on their unique speech signal, with potential applications in security, communication, and personalization. Sentiment analysis is a statistical method that analyzes unique acoustic properties of the speaker's voice to identify emotions or sentiments in speech. This allows for automated speech recognition systems to

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32

Sunilkumar, M. Hattaraki, and M.Sonar Vinodakumar. "A Study on Automatic Speech Recognition." International Journal of Innovative Science and Research Technology 8, no. 4 (2023): 972–73. https://doi.org/10.5281/zenodo.7879813.

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Over the last few decades, speech signal processing has been the most interesting and broad area of research. Speech is an effective means of communication for humans to converse with one another. Speech can be heard by human ears in a real-world environment, even with all of the background noises. Distinguishing the target speech from the background noise by a machine, on the other hand, remains a difficult issue. Man has always had a strong desire to make machines more human-like. This desire has resulted in the development of disciplines such as artificial intelligence (AI), which mimic hum

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33

Rista, Amarildo, and Arbana Kadriu. "Automatic Speech Recognition: A Comprehensive Survey." SEEU Review 15, no. 2 (2020): 86–112. http://dx.doi.org/10.2478/seeur-2020-0019.

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Abstract Speech recognition is an interdisciplinary subfield of natural language processing (NLP) that facilitates the recognition and translation of spoken language into text by machine. Speech recognition plays an important role in digital transformation. It is widely used in different areas such as education, industry, and healthcare and has recently been used in many Internet of Things and Machine Learning applications. The process of speech recognition is one of the most difficult processes in computer science. Despite numerous searches in this domain, an optimal method for speech recogni

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34

Kholiev, Vladislav O., and Olesia Yu Barkovska. "Improved Speaker Recognition System Using Automatic Lip Recognition." Control Systems and Computers, no. 1 (305) (2024): 38–49. http://dx.doi.org/10.15407/csc.2024.01.038.

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The paper is focused on the relevant problem of speech recognition using additional sources besides the voice itself, in conditions in which the quality or availability of audio information is inadequate (for example, in the presence of noise or additional speakers). This is achieved by using automatic lip recognition (ARL) methods, which rely on non-acoustic biosignals generated by the human body during speech production. Among the applications of this approach are medical applications, as well as processing voice commands in languages with poor audio conditions. The aim of this work is to cr

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35

Galatang, Danny Henry, and Suyanto Suyanto. "Syllable-Based Indonesian Automatic Speech Recognition." International Journal on Electrical Engineering and Informatics 12, no. 4 (2020): 720–28. http://dx.doi.org/10.15676/ijeei.2020.12.4.2.

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The syllable-based automatic speech recognition (ASR) systems commonly perform better than the phoneme-based ones. This paper focuses on developing an Indonesian monosyllable-based ASR (MSASR) system using an ASR engine called SPRAAK and comparing it to a phoneme-based one. The Mozilla DeepSpeech-based end-to-end ASR (MDSE2EASR), one of the state-of-the-art models based on character (similar to the phoneme-based model), is also investigated to confirm the result. Besides, a novel Kaituoxu SpeechTransformer (KST) E2EASR is also examined. Testing on the Indonesian speech corpus of 5,439 words sh

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36

GURTUEVA, I. A. "MODERN PROBLEMS OF AUTOMATIC SPEECH RECOGNITION." News of the Kabardin-Balkar Scientific Center of RAS 6, no. 98 (2020): 20–33. http://dx.doi.org/10.35330/1991-6639-2020-6-98-20-33.

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37

Abushariah, Mohammad A. M., and Assal A. M. Alqudah. "Automatic Identity Recognition Using Speech Biometric." European Scientific Journal, ESJ 12, no. 12 (2016): 43. http://dx.doi.org/10.19044/esj.2016.v12n12p43.

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Biometric technology refers to the automatic identification of a person using physical or behavioral traits associated with him/her. This technology can be an excellent candidate for developing intelligent systems such as speaker identification, facial recognition, signature verification...etc. Biometric technology can be used to design and develop automatic identity recognition systems, which are highly demanded and can be used in banking systems, employee identification, immigration, e-commerce…etc. The first phase of this research emphasizes on the development of automatic identity recogniz

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38

Sain, Nakshatra. "Automatic Speech Recognition: - Ease of Typing." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (2018): 1932–34. http://dx.doi.org/10.22214/ijraset.2018.4331.

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39

Mihajlik, Péter, Tibor Révész, and Péter Tatai. "Phonetic transcription in automatic speech recognition." Acta Linguistica Hungarica 49, no. 3-4 (2002): 407–25. http://dx.doi.org/10.1556/aling.49.2002.3-4.9.

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40

Coro, Gianpaolo, Fabio Valerio Massoli, Antonio Origlia, and Francesco Cutugno. "Psycho-acoustics inspired automatic speech recognition." Computers & Electrical Engineering 93 (July 2021): 107238. http://dx.doi.org/10.1016/j.compeleceng.2021.107238.

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41

Aldarmaki, Hanan, Asad Ullah, Sreepratha Ram, and Nazar Zaki. "Unsupervised Automatic Speech Recognition: A review." Speech Communication 139 (April 2022): 76–91. http://dx.doi.org/10.1016/j.specom.2022.02.005.

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42

Sain, Nakshatra. "Automatic Speech Recognition: - A Literature Survey." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (2018): 3644–45. http://dx.doi.org/10.22214/ijraset.2018.4606.

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43

Gjoreski, Martin, Hristijan Gjoreski, and Andrea Kulakov. "Automatic Recognition of Emotions from Speech." International Journal of Computational Linguistics Research 10, no. 4 (2019): 101. http://dx.doi.org/10.6025/jcl/2019/10/4/101-107.

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44

Hartwell, Walter T. "Automatic speech recognition using echo cancellation." Journal of the Acoustical Society of America 91, no. 3 (1992): 1791. http://dx.doi.org/10.1121/1.403754.

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45

Noyes, Jan M., Chris Baber, and Andrew P. Leggatt. "Automatic Speech Recognition, Noise and Workload." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 22 (2000): 762–65. http://dx.doi.org/10.1177/154193120004402269.

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Despite the increasing use of technology in the developed world, most computer communications still take place via a QWERTY keyboard and a mouse. The use of Automatic Speech Recognition (ASR) whereby individuals can ‘talk’ to their computers has yet to be realised to any great extent. This is despite the benefits relating to greater efficiency, use in adverse environments and in the ‘hands-eyes busy’ situation. There are now affordable ASR products in the marketplace, and many people are able to buy these products and try ASR for themselves. However, anecdotal reports suggest that these same p

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46

Martinčić-Ipšić, Sanda, Miran Pobar, and Ivo Ipšić. "Croatian Large Vocabulary Automatic Speech Recognition." Automatika 52, no. 2 (2011): 147–57. http://dx.doi.org/10.1080/00051144.2011.11828413.

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47

Bilmes, Jeff A. "Graphical models and automatic speech recognition." Journal of the Acoustical Society of America 112, no. 5 (2002): 2278–79. http://dx.doi.org/10.1121/1.4779134.

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48

O’Shaughnessy, Douglas, and Selouani Sid‐Ahmed. "Robust automatic recognition of telephone speech." Journal of the Acoustical Society of America 113, no. 4 (2003): 2198. http://dx.doi.org/10.1121/1.4780179.

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49

Levinson, S. E. "Structural methods in automatic speech recognition." Proceedings of the IEEE 73, no. 11 (1985): 1625–50. http://dx.doi.org/10.1109/proc.1985.13344.

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50

O'Shaughnessy, Douglas. "Acoustic Analysis for Automatic Speech Recognition." Proceedings of the IEEE 101, no. 5 (2013): 1038–53. http://dx.doi.org/10.1109/jproc.2013.2251592.

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