Relevant bibliographies by topics / Automatic speech recognition

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  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Automatic speech recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Automatic speech recognition"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Automatic speech recognition"

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Alcaraz, Meseguer Noelia. "Speech Analysis for Automatic Speech Recognition." Thesis, Norwegian University of Science and Technology, Department of Electronics and Telecommunications, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9092.

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<p>The classical front end analysis in speech recognition is a spectral analysis which parametrizes the speech signal into feature vectors; the most popular set of them is the Mel Frequency Cepstral Coefficients (MFCC). They are based on a standard power spectrum estimate which is first subjected to a log-based transform of the frequency axis (mel- frequency scale), and then decorrelated by using a modified discrete cosine transform. Following a focused introduction on speech production, perception and analysis, this paper gives a study of the implementation of a speech generative model; where
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Gabriel, Naveen. "Automatic Speech Recognition in Somali." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166216.

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The field of speech recognition during the last decade has left the research stage and found its way into the public market, and today, speech recognition software is ubiquitous around us. An automatic speech recognizer understands human speech and represents it as text. Most of the current speech recognition software employs variants of deep neural networks. Before the deep learning era, the hybrid of hidden Markov model and Gaussian mixture model (HMM-GMM) was a popular statistical model to solve speech recognition. In this thesis, automatic speech recognition using HMM-GMM was trained on So
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Al-Shareef, Sarah. "Conversational Arabic Automatic Speech Recognition." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/10145/.

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Colloquial Arabic (CA) is the set of spoken variants of modern Arabic that exist in the form of regional dialects and are considered generally to be mother-tongues in those regions. CA has limited textual resource because it exists only as a spoken language and without a standardised written form. Normally the modern standard Arabic (MSA) writing convention is employed that has limitations in phonetically representing CA. Without phonetic dictionaries the pronunciation of CA words is ambiguous, and can only be obtained through word and/or sentence context. Moreover, CA inherits the MSA complex
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Jalalvand, Shahab. "Automatic Speech Recognition Quality Estimation." Doctoral thesis, Università degli studi di Trento, 2017. https://hdl.handle.net/11572/368743.

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Evaluation of automatic speech recognition (ASR) systems is difficult and costly, since it requires manual transcriptions. This evaluation is usually done by computing word error rate (WER) that is the most popular metric in ASR community. Such computation is doable only if the manual references are available, whereas in the real-life applications, it is a too rigid condition. A reference-free metric to evaluate the ASR performance is \textit{confidence measure} which is provided by the ASR decoder. However, the confidence measure is not always available, especially in commercial ASR usages. E
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Jalalvand, Shahab. "Automatic Speech Recognition Quality Estimation." Doctoral thesis, University of Trento, 2017. http://eprints-phd.biblio.unitn.it/2058/1/PhD_Thesis.pdf.

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Evaluation of automatic speech recognition (ASR) systems is difficult and costly, since it requires manual transcriptions. This evaluation is usually done by computing word error rate (WER) that is the most popular metric in ASR community. Such computation is doable only if the manual references are available, whereas in the real-life applications, it is a too rigid condition. A reference-free metric to evaluate the ASR performance is \textit{confidence measure} which is provided by the ASR decoder. However, the confidence measure is not always available, especially in commercial ASR usages. E
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Wang, Peidong. "Robust Automatic Speech Recognition By Integrating Speech Separation." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1619099401042668.

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Seward, Alexander. "Efficient Methods for Automatic Speech Recognition." Doctoral thesis, KTH, Tal, musik och hörsel, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3675.

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This thesis presents work in the area of automatic speech recognition (ASR). The thesis focuses on methods for increasing the efficiency of speech recognition systems and on techniques for efficient representation of different types of knowledge in the decoding process. In this work, several decoding algorithms and recognition systems have been developed, aimed at various recognition tasks. The thesis presents the KTH large vocabulary speech recognition system. The system was developed for online (live) recognition with large vocabularies and complex language models. The system utilizes weight
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Vipperla, Ravichander. "Automatic Speech Recognition for ageing voices." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5725.

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With ageing, human voices undergo several changes which are typically characterised by increased hoarseness, breathiness, changes in articulatory patterns and slower speaking rate. The focus of this thesis is to understand the impact of ageing on Automatic Speech Recognition (ASR) performance and improve the ASR accuracies for older voices. Baseline results on three corpora indicate that the word error rates (WER) for older adults are significantly higher than those of younger adults and the decrease in accuracies is higher for males speakers as compared to females. Acoustic parameters such as
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Guzy, Julius Jonathan. "Automatic speech recognition : a refutation approach." Thesis, De Montfort University, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.254196.

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Deterding, David Henry. "Speaker normalisation for automatic speech recognition." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359822.

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Books on the topic "Automatic speech recognition"

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Yu, Dong, and Li Deng. Automatic Speech Recognition. Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-5779-3.

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Lee, Kai-Fu. Automatic Speech Recognition. Springer US, 1989. http://dx.doi.org/10.1007/978-1-4615-3650-5.

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Woelfel, Matthias. Distant speech recognition. Wiley, 2009.

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Junqua, Jean-Claude, and Jean-Paul Haton. Robustness in Automatic Speech Recognition. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1297-0.

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Lee, Chin-Hui, Frank K. Soong, and Kuldip K. Paliwal, eds. Automatic Speech and Speaker Recognition. Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1367-0.

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Keshet, Joseph, and Samy Bengio, eds. Automatic Speech and Speaker Recognition. John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470742044.

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Huang, X. D. Hidden Markov models for speech recognition. Edinburgh University Press, 1990.

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Markowitz, Judith A. Using speech recognition. Prentice Hall PTR, 1996.

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Ainsworth, W. A. Speech recognition by machine. P. Peregrinus on behalf of the Institution of Electrical Engineers, 1988.

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Ainsworth, W. A. Speech recognition by machine. Peregrinus on behalf of the Institution of Electrical Engineers, 1987.

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Book chapters on the topic "Automatic speech recognition"

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Kurematsu, Akira, and Tsuyoshi Morimoto. "Speech Recognition." In Automatic Speech Translation. CRC Press, 2023. http://dx.doi.org/10.1201/9780429333385-2.

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Lu, Xugang, Sheng Li, and Masakiyo Fujimoto. "Automatic Speech Recognition." In SpringerBriefs in Computer Science. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0595-9_2.

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Owens, F. J. "Automatic Speech Recognition." In Signal Processing of Speech. Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-22599-6_7.

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Schäuble, Peter. "Automatic Speech Recognition." In Multimedia Information Retrieval. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6163-7_4.

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Soltau, Hagen, George Saon, Lidia Mangu, et al. "Automatic Speech Recognition." In Natural Language Processing of Semitic Languages. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45358-8_13.

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Chowdhary, K. R. "Automatic Speech Recognition." In Fundamentals of Artificial Intelligence. Springer India, 2020. http://dx.doi.org/10.1007/978-81-322-3972-7_20.

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Gruhn, Rainer E., Wolfgang Minker, and Satoshi Nakamura. "Automatic Speech Recognition." In Signals and Communication Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19586-0_2.

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Kamath, Uday, John Liu, and James Whitaker. "Automatic Speech Recognition." In Deep Learning for NLP and Speech Recognition. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14596-5_8.

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Weik, Martin H. "automatic speech recognition." In Computer Science and Communications Dictionary. Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_1147.

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Potamianos, Gerasimos, Lori Lamel, Matthias Wölfel, et al. "Automatic Speech Recognition." In Computers in the Human Interaction Loop. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-054-8_6.

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Conference papers on the topic "Automatic speech recognition"

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Min, Do June, Karel Mundnich, Andy Lapastora, Erfan Soltanmohammadi, Srikanth Ronanki, and Kyu Han. "Speech Retrieval-Augmented Generation without Automatic Speech Recognition." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888900.

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Tian, Jingguang, Shuaishuai Ye, Shunfei Chen, et al. "The Royalflush Automatic Speech Diarization and Recognition System for In-Car Multi-Channel Automatic Speech Recognition Challenge." In 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2024. http://dx.doi.org/10.1109/icasspw62465.2024.10626136.

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O'Shaughnessy, Douglas. "Automatic speech recognition." In 2015 Chilean Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON). IEEE, 2015. http://dx.doi.org/10.1109/chilecon.2015.7400411.

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Glasser, Abraham. "Automatic Speech Recognition Services." In CHI '19: CHI Conference on Human Factors in Computing Systems. ACM, 2019. http://dx.doi.org/10.1145/3290607.3308461.

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Catariov, Alexandru. "Automatic speech recognition systems." In Chisinau - DL tentative, edited by Andrei M. Andriesh and Veacheslav L. Perju. SPIE, 2005. http://dx.doi.org/10.1117/12.612047.

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Paulik, M., S. Stuker, C. Fugen, T. Schultz, T. Schaaf, and A. Waibel. "Speech translation enhanced automatic speech recognition." In IEEE Workshop on Automatic Speech Recognition and Understanding, 2005. IEEE, 2005. http://dx.doi.org/10.1109/asru.2005.1566488.

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Ahmed, Basem H. A., and Ayman S. Ghabayen. "Arabic Automatic Speech Recognition Enhancement." In 2017 Palestinian International Conference on Information and Communication Technology (PICICT). IEEE, 2017. http://dx.doi.org/10.1109/picict.2017.12.

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Adi, Derry Pramono, Agustinus Bimo Gumelar, and Ralin Pramasuri Arta Meisa. "Interlanguage of Automatic Speech Recognition." In 2019 International Seminar on Application for Technology of Information and Communication (iSemantic). IEEE, 2019. http://dx.doi.org/10.1109/isemantic.2019.8884310.

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Anoop, C. S., and A. G. Ramakrishnan. "Automatic Speech Recognition for Sanskrit." In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2019. http://dx.doi.org/10.1109/icicict46008.2019.8993283.

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Munteanu, Cosmin, Gerald Penn, Ron Baecker, and Yuecheng Zhang. "Automatic speech recognition for webcasts." In the 8th international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1180995.1181005.

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Reports on the topic "Automatic speech recognition"

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Clements, Mark A., John H. Hansen, Kathleen E. Cummings, and Sungjae Lim. Automatic Recognition of Speech in Stressful Environments. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada242917.

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Brown, Peter F. The Acoustic-Modeling Problem in Automatic Speech Recognition. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada188529.

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Vergyri, Dimitra, and Katrin Kirchhoff. Automatic Diacritization of Arabic for Acoustic Modeling in Speech Recognition. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada457846.

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Bass, James D. Advancing Noise Robust Automatic Speech Recognition for Command and Control Applications. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada461436.

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Stevenson, G. Analysis of Pre-Trained Deep Neural Networks for Large-Vocabulary Automatic Speech Recognition. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1289367.

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Fatehifar, Mohsen, Josef Schlittenlacher, David Wong, and Kevin Munro. Applications Of Automatic Speech Recognition And Text-To-Speech Models To Detect Hearing Loss: A Scoping Review Protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2023. http://dx.doi.org/10.37766/inplasy2023.1.0029.

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Review question / Objective: This scoping review aims to identify published methods that have used automatic speech recognition or text-to-speech recognition technologies to detect hearing loss and report on their accuracy and limitations. Condition being studied: Hearing enables us to communicate with the surrounding world. According to reports by the World Health Organization, 1.5 billion suffer from some degree of hearing loss of which 430 million require medical attention. It is estimated that by 2050, 1 in every 4 people will experience some sort of hearing disability. Hearing loss can si
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Oran, D. Requirements for Distributed Control of Automatic Speech Recognition (ASR), Speaker Identification/Speaker Verification (SI/SV), and Text-to-Speech (TTS) Resources. RFC Editor, 2005. http://dx.doi.org/10.17487/rfc4313.

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Tao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir, and Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7594391.bard.

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Extending the previous two years of research results (Mizarch, et al, 2012, Tao, 2011, 2012), the third year’s efforts in both Maryland and Israel were directed towards the engineering of the system. The activities included the robust chick handling and its conveyor system development, optical system improvement, online dynamic motion imaging of chicks, multi-image sequence optimal feather extraction and detection, and pattern recognition. Mechanical System Engineering The third model of the mechanical chick handling system with high-speed imaging system was built as shown in Fig. 1. This syst
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Issues in Data Processing and Relevant Population Selection. OSAC Speaker Recognition Subcommittee, 2022. http://dx.doi.org/10.29325/osac.tg.0006.

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In Forensic Automatic Speaker Recognition (FASR), forensic examiners typically compare audio recordings of a speaker whose identity is in question with recordings of known speakers to assist investigators and triers of fact in a legal proceeding. The performance of automated speaker recognition (SR) systems used for this purpose depends largely on the characteristics of the speech samples being compared. Examiners must understand the requirements of specific systems in use as well as the audio characteristics that impact system performance. Mismatch conditions between the known and questioned
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