Relevant bibliographies by topics / Whisper ASR

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Whisper ASR'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Whisper ASR"

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Galić, Jovan, Branko Marković, Đorđe Grozdić, Branislav Popović, and Slavko Šajić. "Whispered Speech Recognition Based on Audio Data Augmentation and Inverse Filtering." Applied Sciences 14, no. 18 (2024): 8223. http://dx.doi.org/10.3390/app14188223.

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Modern Automatic Speech Recognition (ASR) systems are primarily designed to recognize normal speech. Due to a considerable acoustic mismatch between normal speech and whisper, ASR systems suffer from a significant loss of performance in whisper recognition. Creating large databases of whispered speech is expensive and time-consuming, so research studies explore the synthetic generation using pre-existing normal or whispered speech databases. The impact of standard audio data augmentation techniques on the accuracy of isolated-word recognizers based on Hidden Markov Models (HMM) and Convolution
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Attia, Ahmed Adel, Jing Liu, Wei Ai, Dorottya Demszky, and Carol Espy-Wilson. "Kid-Whisper: Towards Bridging the Performance Gap in Automatic Speech Recognition for Children VS. Adults." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 74–80. http://dx.doi.org/10.1609/aies.v7i1.31618.

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Recent advancements in Automatic Speech Recognition (ASR) systems, exemplified by Whisper, have demonstrated the potential of these systems to approach human-level performance given sufficient data. However, this progress doesn’t readily extend to ASR for children due to the lim- ited availability of suitable child-specific databases and the distinct characteristics of children’s speech. A recent study investigated leveraging the My Science Tutor (MyST) chil- dren’s speech corpus to enhance Whisper’s performance in recognizing children’s speech. They were able to demon- strate some improvement
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Si, Mei, Omar Cobas, and Michael Fababeir. "Lexical Error Guard: Leveraging Large Language Models for Enhanced ASR Error Correction." Machine Learning and Knowledge Extraction 6, no. 4 (2024): 2435–46. http://dx.doi.org/10.3390/make6040120.

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Error correction is a vital element in modern automatic speech recognition (ASR) systems. A significant portion of ASR error correction work is closely integrated within specific ASR systems, which creates challenges for adapting these solutions to different ASR frameworks. This research introduces Lexical Error Guard (LEG), which leverages the extensive pre-trained knowledge of large language models (LLMs) and employs instructional learning to create an adaptable error correction system compatible with various ASR platforms. Additionally, a parameter-efficient fine-tuning method is utilized u
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Papala, Gowtham, Aniket Ransing, and Pooja Jain. "Sentiment Analysis and Speaker Diarization in Hindi and Marathi Using using Finetuned Whisper." Scalable Computing: Practice and Experience 24, no. 4 (2023): 835–46. http://dx.doi.org/10.12694/scpe.v24i4.2248.

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Automatic Speech Recognition (ASR) is a crucial technology that enables machines to automatically recognize human voices based on audio signals. In recent years, there has been a rigorous growth in the development of ASR models with the emergence of new techniques and algorithms. One such model is the Whisper ASR model developed by OpenAI, which is based on a Transformer encoder-decoder architecture and can handle multiple tasks such as language identification, transcription, and translation. However, there are still limitations to the Whisper ASR model, such as speaker diarization, summarizat
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Saraf, Aryan. "Multilingual Translation for Speech and Text using Whisper AI: A Deep Learning Approach." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 1895–901. https://doi.org/10.22214/ijraset.2025.73288.

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In an increasingly interconnected world, the ability to accurately translate between multiple languages, both written and spoken, is essential for global communication. Traditional machine translation and speech recognition systems often operate as separate pipelines, leading to increased complexity and reduced efficiency, especially when dealing with low-resource languages or noisy audio environments. This research presents a comprehensive study of Whisper AI, a multilingual, multitask model developed by OpenAI for speech recognition and translation. Leveraging a transformer-based encoder-dec
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Ghale, Akarsh, Janaki K, and Devaraj Verma C. "Instant Transcription and Translation Tool using OpenAI?s Whisper ASR Model." International Journal of Science and Research (IJSR) 11, no. 12 (2022): 185–88. http://dx.doi.org/10.21275/sr221203164929.

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Pratama, Riefkyanov Surya Adia, and Agit Amrullah. "ANALYSIS OF WHISPER AUTOMATIC SPEECH RECOGNITION PERFORMANCE ON LOW RESOURCE LANGUAGE." Jurnal Pilar Nusa Mandiri 20, no. 1 (2024): 1–8. http://dx.doi.org/10.33480/pilar.v20i1.4633.

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Implementing Automatic Speech Recognition Technology in daily life could give convenience to its users. However, speeches that can be recognized accurately by the ASR model right now are in languages considered high resources, like English. In previous research, a few regional languages like Javanese, Sundanese, Balinese and Btaknese are used in automatic speech recognition. This research aim is to improve speech recognition using the ASR model on low-resource language. The dataset used in this research is the Javanese dataset specifically because there is a high-quality Javanese speech datase
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Polat, Hüseyin, Alp Kaan Turan, Cemal Koçak, and Hasan Basri Ulaş. "Implementation of a Whisper Architecture-Based Turkish Automatic Speech Recognition (ASR) System and Evaluation of the Effect of Fine-Tuning with a Low-Rank Adaptation (LoRA) Adapter on Its Performance." Electronics 13, no. 21 (2024): 4227. http://dx.doi.org/10.3390/electronics13214227.

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This paper focuses on the implementation of the Whisper architecture to create an automatic speech recognition (ASR) system optimized for the Turkish language, which is considered a low-resource language in terms of speech recognition technologies. Whisper is a transformer-based model known for its high performance across numerous languages. However, its performance in Turkish, a language with unique linguistic features and limited labeled data, has yet to be fully explored. To address this, we conducted a series of experiments using five different Turkish speech datasets to assess the model’s
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Maurya, Maruti, Mohd Zaheer, Nawab Mohammad, Sadaf siddiqui, Mohd Zeeshan Khan, and Mohd Ayan Akram. "Speech Recognition Technologies: Design, Challenges, and Real-World Applications." International Journal of Innovative Research in Computer Science and Technology 13, no. 3 (2025): 55–61. https://doi.org/10.55524/ijircst.2025.13.3.9.

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This paper presents an automated speech recognition (ASR) system that transcribes audio from YouTube videos into accurate text using OpenAI's Whisper model. Leveraging tools such as yt_dlp, FFmpeg, and PyTorch, the system creates a robust speech-to-text pipeline. On receiving a video URL, the system extracts and preprocesses audio, transcribes it using Whisper, and evaluates transcription quality through metrics like Word Error Rate (WER), Character Error Rate (CER), and Match Error Rate (MER). The pipeline supports offline use, making it suitable for accessible, cost-effective deployment in e
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Lee, Sangmin, Woojin Chung, and Hong-Goo Kang. "LAMA-UT: Language Agnostic Multilingual ASR Through Orthography Unification and Language-Specific Transliteration." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 23 (2025): 24393–401. https://doi.org/10.1609/aaai.v39i23.34617.

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Building a universal multilingual automatic speech recognition (ASR) model that performs equitably across languages has long been a challenge due to its inherent difficulties. To address this task we introduce a Language-Agnostic Multilingual ASR pipeline through orthography Unification and language-specific Transliteration (LAMA-UT). LAMA-UT operates without any language-specific modules while matching the performance of state-of-the-art models trained on a minimal amount of data. Our pipeline consists of two key steps. First, we utilize a universal transcription generator to unify orthograph
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Dissertations / Theses on the topic "Whisper ASR"

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Erven, Lisa N. "An observational study of slope air and free air wintertime temperatures in Whistler Valley, British Columbia, Canada." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42468.

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Temperature structure within complex terrain is fundamental to determining stability, thermally-induced circulations, and mountain weather, all of which impact those living, working, and recreating within it, as well as those external to it that depend on water from snowmelt. While numerous studies and text books outline many factors affecting slope air and free air temperatures, the interactions of these factors with the complex terrain makes predictability of temperature structures very difficult. This is further compounded by sparse observational data that has limited representativeness due
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Gallagher, John Patrick. "Patterns of planetary boundary layer influence at the Whistler Mountain air chemistry observatory : an observational mountain meteorology study." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/28803.

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An observational study was conducted to characterize atmospheric conditions at an air chemistry monitoring site on the summit of Whistler Mountain, British Columbia, Canada. Discrimination of air samples from the observatory as either representative of the free troposphere (FT) or modified by air from the valley-based planetary boundary layer (PBL) is critical to the proper interpretation of air chemistry datasets. Atmospheric data from a one-year study period were used to evaluate indicators and possible driving forces of PBL influence at the Whistler site. Diurnal cycles in water vapour and
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Liu, Hsiao-Hong, and 劉孝虹. "The Mechanism of Oxide Whisker Growth and High Temperature Corrosion of Al-Si Coated 430 Stainless Steels in Air-NaCl(g) Atmosphere." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/mydq9k.

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碩士<br>國立臺灣科技大學<br>機械工程系<br>94<br>The mechanisms of whisker growth and high temperature corrosion of 430 stainless steels(430SS) with/without aluminum-silicon coated were studied at 750℃ and 850℃ in air/air-NaCl(g)(500, 990 vppm) atmosphere. The results showed that the porous Cr2O3 scale could not prevent corrosion in 500 vppm NaCl(g) atmosphere. The Fe2O3 whisker grew on the surface due to the accelerated diffusion of ions in the scale by the continuous supply of NaCl(g) atmosphere. There is no Fe2O3 whisker on the scale surface in the higher concentration of 990 vppm NaCl(g) atmosphere. A co
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Books on the topic "Whisper ASR"

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Mackall, Dandi Daley. Horse whispers in the air. Concordia Pub. House, 2000.

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MacLeod, Mary K. Whisper in the air: Maerconi - the Canada years, 1902-1946. Lancelot, 1992.

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Cordingly, Norman. From a cat's whisker beginning--. Merlin Books, 1988.

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Northwind Tradition of American Wicca. The whisper: News & gossip of northwind : Oak, Ash, & Thorn : dragon's gate & allied traditions. Northwind Tradition of American Wicca, 1993.

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Avery, Norman. Whiskey whiskey papa: Chronicling the exciting life and times of a pilot's pilot. N. Avery, 1998.

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Breithaupt, Bren. Whispers in the Air. Independent Publisher, 2013.

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Abengowe, Chikezie, and Omenogor. Whispers in the Air. Page Publishing Inc., 2022.

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illustrator, Shirai Eiri, McCann Sean translator, Seven Seas Entertainment LLC, J.-Novel Club, and Macmillan Publishers, eds. Grimgar of Fantasy and Ash: Whisper, Chant, Prayer, Awaken. Airship, 2017.

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Whisper in the air: Marconi, the Canada years, 1902-1946. Lancelot Press, 1992.

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Cordingly, Norman. From a Cat's Whisker Beginning. Hyperion Books, 1996.

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Book chapters on the topic "Whisper ASR"

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Hanawalt, Christina, and Brooke Hofsess. "From Whispers to Screams: Gifts + Provocations." In Reconceptualizing Early Career Teacher Mentoring as Reggio-Inspired. Routledge, 2023. http://dx.doi.org/10.4324/9781003195566-4.

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Kara, F., and J. A. Little. "Oxidation of SiC Whisker-reinforced-Mullite in Dry and Wet Air." In Microscopy of Oxidation, 2nd ed. CRC Press, 2024. http://dx.doi.org/10.1201/9781003575825-61.

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Arrott, A. S., B. Heinrich, and S. T. Purcell. "Rheed Intensities and Oscillations During the Growth of Iron on Iron Whiskers." In NATO ASI Series. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-0653-5_21.

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"Hot Air." In Teach Me How to Whisper. Syracuse University Press, 2023. http://dx.doi.org/10.2307/jj.7193901.56.

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Salabert, Juana. "YOU’LL BECOME A WHISPER OF AIR." In Rainy Days / Dias de Lluvia. Liverpool University Press, 2018. http://dx.doi.org/10.2307/j.ctv16zjzkx.20.

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Catford, J. C. "Articulation: Stricture Types." In A Practical Introduction to Phonetics. Oxford University PressOxford, 2001. http://dx.doi.org/10.1093/oso/9780199246359.003.0004.

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Abstract We turn now to the third basic component of speech production— articulation. As we have seen, initiation sets up an air-stream in the vocal tract. For sounds with an air-stream flowing through the larynx, phonation imparts a general modulation to the sound, making it voiceless, voiced, whispered . . . etc. The important function of articulation is to impose upon the (unphonated or phonated) air-stream a final ‘shaping’, as it were, so as to generate a sound of specific type and quality.
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Joyner, Charles. "Stvron’s Choice: A Meditation on History, Literature, and Moral Imperatives." In Nat Turner. Oxford University PressNew York, NY, 2004. http://dx.doi.org/10.1093/oso/9780195177565.003.0011.

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Abstract The day dawned bleak and chill that Friday in the Virginia tidewater, and an enveloping gray light seemed to come out of the northeast. The dry leaves whispered a little in the windless November. Around noon the jailer unlocked the condemned hole of the Southampton County Jail. It was cold and musty in the hole, and the rank smell fouled the air.
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"HAVE YOU EVER HEARD OF A HORSE WHISPERER?" In Light As Light. University of Arizona Press, 2023. http://dx.doi.org/10.2307/jj.7941381.53.

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Godden, Richard. "Introduction." In Fictions of Finance at the End of an American Century. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780192867759.003.0001.

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Abstract A book about labor and language, and their interrelation in a time of emergent finance (1973–2007), Fictions of Finance at the End of an American Century begins by establishing historical and theoretical grounds for their interrelation. Departing from Marx’s account of language as “practical consciousness” made from “agitated air,” the introduction centrally does two things: first, it assesses the sources of agitation, registered within the process of capitalist value production, as finance “apparently” displaces manufacture from value’s core. Second, to establish language as a “pract
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"Ruskin vs. Whistler: The Case against Capitalist Art." In Art History as Social Praxis. BRILL, 2017. http://dx.doi.org/10.1163/9789004235861_011.

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Conference papers on the topic "Whisper ASR"

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Polok, Alexander, Dominik Klement, Matthew Wiesner, Sanjeev Khudanpur, Jan Černocký, and Lukáš Burget. "Target Speaker ASR with Whisper." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10887683.

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P, Thara, Mohammed Azneed, Muhammad Sanas, Jithu Prakash P. M, Harsh P. Naik, and Divya B. "Subtitle Synchronization Using Whisper ASR Model." In 2024 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2024. https://doi.org/10.1109/icpects62210.2024.10780268.

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Thorbecke, Iuliia, Juan Pablo Zuluaga Gomez, Esaú Villatoro-tello, et al. "Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.976.

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Barański, Mateusz, Jan Jasiński, Julitta Bartolewska, Stanisław Kacprzak, Marcin Witkowski, and Konrad Kowalczyk. "Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890105.

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Segal-Feldman, Yael, Aviv Shamsian, Aviv Navon, Gill Hetz, and Joseph Keshet. "Whisper in Medusa’s Ear: Multi-head Efficient Decoding for Transformer-based ASR." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888140.

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Lin, Guojian, Yu Tsao, and Fei Chen. "A Non-Intrusive Speech Quality Assessment Model using Whisper and Multi-Head Attention." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10848735.

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Chou, Huang-Cheng. "A Tiny Whisper-SER: Unifying Automatic Speech Recognition and Multi-label Speech Emotion Recognition Tasks." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10848651.

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Yang, Yuhang, Yizhou Peng, Hao Huang, Eng Siong Chng, and Xionghu Zhong. "Adapting OpenAI’s Whisper for Speech Recognition on Code-Switch Mandarin-English SEAME and ASRU2019 Datasets." In 2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2024. https://doi.org/10.1109/apsipaasc63619.2025.10849308.

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Marković, Branko R., and Đorđe Damnjanović. "Experiments in Whispered Speech Recognition Based on Wavelet Transformation." In International Conference IcETRAN. ETRAN Society, Academic Mind, Belgrade, 2024. https://doi.org/10.69994/11ic24002.

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In this paper the results of normal and whispered speech recognition based on DWT (Discrete Wavelet Transformation) are presented. The feature vectors are obtained based on Daubechies sub-band energy. The experiments are performed using a part of the Whi-Spe database (one female and one male speaker). A back-end of the ASR system is based on DTW (Dynamic Time Warping) algorithm. The following scenarios are analyzed: normal/normal, whisper/whisper, normal/whisper and whisper/normal in the speaker dependent mode. The results confirmed the usefulness of Wavelet transformation in speech recognitio
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El Ayari, Sarra, and Zhongjie Li. "Potential of ASR for the study of L2 learner corpora." In 13th Workshop on Natural Language Processing for Computer Assisted Language Learning. Linköping University Electronic Press, 2024. http://dx.doi.org/10.3384/ecp211004.

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This study is at the crossroads of Natural Language Processing (NLP) and Second Language Acquistion (SLA). We used Word Error Rate (WER) measurements of Whisper's speech recognition on a French L2 learner corpus to get automatic transcripts, and compared them with pre-existing manual transcripts. We then conducted quantitative and qualitative analysis of the issues which are inherent to the specificities of interlanguage for any automatic tool. We will discuss the different issues encountered by Whisper that are specific to learner corpora.
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Reports on the topic "Whisper ASR"

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AIR FORCE DISTRICT OF WASHINGTON. Environmental Assessment for Taxiway Whiskey Supplemental Projects at Joint Base Andrews-Naval Air Facility Washington, Prince George's County, Maryland. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada628457.

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Cannella, Michelle, Jennifer Jarvis, Tim Lavallee, et al. Environmental Assessment for Replacement of Taxiway Sierra, Taxiway Whiskey, Pad 12, and Pad 13 at Joint Base Andrews-Naval Air Facility Washington, Prince George's County, Maryland. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada612757.

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MacFarlane, Andrew. 2021 medical student essay prize winner - A case of grief. Society for Academic Primary Care, 2021. http://dx.doi.org/10.37361/medstudessay.2021.1.1.

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As a student undertaking a Longitudinal Integrated Clerkship (LIC)1 based in a GP practice in a rural community in the North of Scotland, I have been lucky to be given responsibility and my own clinic lists. Every day I conduct consultations that change my practice: the challenge of clinically applying the theory I have studied, controlling a consultation and efficiently exploring a patient's problems, empathising with and empowering them to play a part in their own care2 – and most difficult I feel – dealing with the vast amount of uncertainty that medicine, and particularly primary care, pre
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