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Dataset results
35 results for “speech recognition”
Stream Segregation and Speech Recognition in Noise in Individuals With Cochlear Implants
ClinicalTrials.gov study NCT04854031. IPD Sharing: YES. Countries: 1. Publications: 0.
Data from: Monopolar detection thresholds predict spatial selectivity of neural excitation in cochlear implants: implications for speech recognition
Open the record for dataset details and reuse information.
Nganasan and Kamas Speech Recognition Models
<p>These are the models trained in our paper</p> <p>Partanen, N., Hämäläinen, M. and Klooster, T. (2020) Speech Recognition for Endangered and Extinct Samoyedic languages. In <em>Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation</em>.</p> <p>See the readme for more</p> <p><sub>Based on corpora from</sub></p> <p><sub>Gusev, Valentin; Klooster, Tiina; Wagner-Nagy, Beáta. 2019. "INEL Kamas Corpus." Version 1.0. Publication date 2019-12-15. http://hdl.handle.net/11022/0000-0007-DA6E-9. Archived in Hamburger Zentrum für Sprachkorpora. In: Wagner-Nagy, Beáta; Arkhipov, Alexandre; Ferger, Anne; Jettka, Daniel; Lehmberg, Timm (eds.). The INEL corpora of indigenous Northern Eurasian languages.</sub></p> <p><sub>Maria Brykina, Valentin Gusev, Sandor Szeverényi, and Beáta Wagner-Nagy. 2018. Nganasan spoken language corpus (nslc). Archived in Hamburger Zentrumfür Sprachkorpora. Version 0.2. Publication date, 12.</sub></p>
The Makerere Radio Speech Corpus: A Luganda Radio Corpus for Automatic Speech Recognition
<p>The Makerere AI Lab has built an end-to-end CTC Luganda ASR model using radio data. Having encountered data challenges in working with low resource languages, we take the initiative together with our partners to release the first radio corpus for Luganda.</p> <p>The corpus of 155 hours is publicly available online under the Creative Commons BY-NC-ND 4.0 license. The dataset release is comprised of the following:</p> <ol> <li>20 hours of human transcribed radio speech. The audio is 16kHZ, mono channel and with 16 bit rate. </li> <li>Two CSV files for the 20-hour human transcribed dataset - cleaned.csv contains cleaned transcripts and uncleaned.csv contains uncleaned transcripts. The uncleaned transcripts contain extra speech details included in tags like [laughter] for laughter, and [um] for filler pauses, which speaker is talking, where each speaker is assigned an identifier A or B.</li> <li>The transcription guide used to transcribe the radio dataset.</li> <li> A multi-speaker untranscribed dataset of 6 hours of radio data. 1.4 hours of women voices and 4.6 hours of men voices. Each audio is a ten-seconds clip with a single speaker.</li> <li> 135 hours of multi-speaker untranscribed radio data.</li> </ol> <p><strong>NOTE: You can read and cite our paper published in the </strong><a href="http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.208.pdf"><strong>Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022)</strong></a> The Dataset is published under Creative Commons BY-NC-ND 4.0 license and in order for us to monitor who is using it for the right license we request that you reach out to us officially. </p>
Multilingual test set for language identification and speech recognition from European Parliament recordings
<p>This test set for language identification and speech recognition is composed by multilingual extracts from European Parliament sessions recordings. </p> <p><strong>Dataset description</strong></p> <p>Audio files and official transcripts were downloaded from: https://www.europarl.europa.eu/plenary/en/debates-video.html</p> <p>The test set has a duration of 02h 56m 34s, composed by 15 multilingual audio files of around 12 minutes, selected from the original material to maximize the number of language changes. </p> <p>Official language labels were manually reviewed to fix start/end timestamps, and official text transcripts, where present, were added to the annotation.</p> <p>The test set covers 19 languages in total.</p> <p>The test set is presented in the following paper:</p> <p>M. Valente, F. Brugnara, G. Morrone, E. Zovato, L. Badino, "Exploring Spoken Language Identification Strategies for Automatic Transcription of Multilingual Broadcast and Institutional Speech", accepted to Interspeech 2024.</p> <p>For more information please refer to the README.txt in the testset .zip archive.</p> <p><strong>License and copyright</strong></p> <p>The data is released with CC0 license: https://creativecommons.org/public-domain/cc0/<br>For the raw data, see also European Parliament's legal notice: https://www.europarl.europa.eu/legal-notice/en/</p>
Effect of Hearing Aid Versus Cochlear Implant on Hearing and Speech Recognition in Children
ClinicalTrials.gov study NCT06913517. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Stimulus Validity on Speech Recognition
ClinicalTrials.gov study NCT00013364. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of Donepezil on Speech Recognition in Cochlear Implant Users
ClinicalTrials.gov study NCT05438264. IPD Sharing: NO. Countries: 1. Publications: 0.
Robotic and Manual Cochlear Implantation: An Intra-individual Study of Speech Recognition and Electrode Holder Position (ICRobMan)
ClinicalTrials.gov study NCT06235073. IPD Sharing: YES. Countries: 1. Publications: 0.
Computer-based Tutorial and Automated Speech Recognition for Intravitreal Drug Injections
ClinicalTrials.gov study NCT04142164. IPD Sharing: NO. Countries: 1. Publications: 0.
Noise-augmented Automatic Speech Recognition for Speech Treatment in Parkinson's Disease
ClinicalTrials.gov study NCT06540989. IPD Sharing: NO. Countries: 1. Publications: 0.
Cochlear Implant Speech and Non-speech Sound Recognition
ClinicalTrials.gov study NCT03661970. IPD Sharing: NO. Countries: 1. Publications: 0.
Inner Speech Recognition for Mutism and Speech Disorder Using Brain-Computer Interface
<p>Four native English speakers, right-handed and healthy individuals participated in collecting EEG-based inner speech data in this study, all of them had no neurological or movement disorders, no hearing loss, and no speech loss. The participants were two males and two females aged 20 to 56 and were named (sub-01) for the first subject (sub-02) for the second subject and so on. A total of 400 recordings sessions were successfully completed, 100 recordings for the command Up, 100 recordings for Down, 100 recordings for the command Left, and 100 recordings for Right.</p>
A lightweight speech recognition method with target-swap knowledge distillation for Mandarin air traffic control communications
<p>Mandarin air traffic control communications (ATCC) dataset for the paper "A lightweight speech recognition method with target-swap knowledge distillation for Mandarin air traffic control communications".</p>
Improved Speech Recognition Performance in Noise by Encoding Binaural Spatial Cues to the Cochlear Implant User.
ClinicalTrials.gov study NCT04357704. IPD Sharing: NO. Countries: 0. Publications: 0.
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International Brain Laboratory public data
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OpenNeuro
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