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859 results for “Speeches”

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zenodo28/100

Hate Speech

Open the record for dataset details and reuse information.

opencc-by-4.0Jul 2024View details →
zenodo28/100

original_speech

<p>This is the system which uses original speech.</p>

opencc-by-4.0Mar 2018View details →
zenodo28/100

Fourteen-channel EEG with Imagined Speech (FEIS) dataset

<pre>&gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; Welcome to the FEIS (Fourteen-channel EEG with Imagined Speech) dataset. &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; The FEIS dataset comprises Emotiv EPOC+ [1] EEG recordings of: * 21 participants listening to, imagining speaking, and then actually speaking 16 English phonemes (see supplementary, below) * 2 participants listening to, imagining speaking, and then actually speaking 16 Chinese syllables (see supplementary, below) For replicability and for the benefit of further research, this dataset includes the complete experiment set-up, including participants&#39; recorded audio and &#39;flashcard&#39; screens for audio-visual prompts, Lua script and .mxs scenario for the OpenVibe [2] environment, as well as all Python scripts for the preparation and processing of data as used in the supporting studies (submitted in support of completion of the MSc Speech and Language Processing with the University of Edinburgh): * J. Clayton, &quot;Towards phone classification from imagined speech using a lightweight EEG brain-computer interface,&quot; M.Sc. dissertation, University of Edinburgh, Edinburgh, UK, 2019. * S. Wellington, &quot;An investigation into the possibilities and limitations of decoding heard, imagined and spoken phonemes using a low-density, mobile EEG headset,&quot; M.Sc. dissertation, University of Edinburgh, Edinburgh, UK, 2019. Each participant&#39;s data comprise 5 .csv files -- these are the &#39;raw&#39; (unprocessed) EEG recordings for the &#39;stimuli&#39;, &#39;articulators&#39; (see supplementary, below) &#39;thinking&#39;, &#39;speaking&#39; and &#39;resting&#39; phases per epoch for each trial -- alongside a &#39;full&#39; .csv file with the end-to-end experiment recording (for the benefit of calculating deltas). To guard against software deprecation or inaccessability, the full repository of open-source software used in the above studies is also included. We hope for the FEIS dataset to be of some utility for future researchers, due to the sparsity of similar open-access databases. As such, this dataset is made freely available for all academic and research purposes (non-profit). &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; REFERENCING &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; If you use the FEIS dataset, please reference: * S. Wellington, J. Clayton, &quot;Fourteen-channel EEG with Imagined Speech (FEIS) dataset,&quot; v1.0, University of Edinburgh, Edinburgh, UK, 2019. doi:10.5281/zenodo.3369178 &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; LEGAL &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; The research supporting the distribution of this dataset has been approved by the PPLS Research Ethics Committee, School of Philosophy, Psychology and Language Sciences, University of Edinburgh (reference number: 435-1819/2). This dataset is made available under the Open Data Commons Attribution License (ODC-BY): <a href="http://opendatacommons.org/licenses/by/1.0">http://opendatacommons.org/licenses/by/1.0</a> &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; ACKNOWLEDGEMENTS &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; The FEIS database was compiled by: Scott Wellington (MSc Speech and Language Processing, University of Edinburgh) Jonathan Clayton (MSc Speech and Language Processing, University of Edinburgh) Principal Investigators: Oliver Watts (Senior Researcher, CSTR, University of Edinburgh) Cassia Valentini-Botinhao (Senior Researcher, CSTR, University of Edinburgh) &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; METADATA &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; For participants, dataset refs 01 to 21: 01 - NNS 02 - NNS 03 - NNS, Left-handed 04 - E 05 - E, Voice heard as part of &#39;stimuli&#39; portions of trials belongs to particpant 04, due to microphone becoming damaged and unusable prior to recording 06 - E 07 - E 08 - E, Ambidextrous 09 - NNS, Left-handed 10 - E 11 - NNS 12 - NNS, Only sessions one and two recorded (out of three total), as particpant had to leave the recording session early 13 - E 14 - NNS 15 - NNS 16 - NNS 17 - E 18 - NNS 19 - E 20 - E 21 - E E = native speaker of English NNS = non-native speaker of English (&gt;= C1 level) For participants, dataset refs chinese-1 and chinese-2: chinese-1 - C chinese-2 - C, Voice heard as part of &#39;stimuli&#39; portions of trials belongs to participant chinese-1 C = native speaker of Chinese &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; SUPPLEMENTARY &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; Under the international 10-20 system, the Emotiv EPOC+ headset 14 channels: F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 The 16 English phonemes investigated in dataset refs 01 to 21: /i/ /u:/ /&aelig;/ /ɔ:/ /m/ /n/ /ŋ/ /f/ /s/ /ʃ/ /v/ /z/ /ʒ/ /p /t/ /k/ The 16 Chinese syllables investigated in dataset refs chinese-1 and chinese-2: mā m&aacute; mǎ m&agrave; mēng m&eacute;ng měng m&egrave;ng duō du&oacute; duǒ du&ograve; tuī tu&iacute; tuǐ tu&igrave; All references to &#39;articulators&#39; (e.g. as part of filenames) refer to the 1-second &#39;fixation point&#39; portion of trials. The name is a layover from preliminary trials which were modelled on the KARA ONE database (<a href="http://www.cs.toronto.edu/~complingweb/data/karaOne/karaOne.html">http://www.cs.toronto.edu/~complingweb/data/karaOne/karaOne.html</a>) [3]. &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &lt;&gt;&lt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; &gt;&lt;&gt; [1] Emotiv EPOC+. <a href="https://emotiv.com/epoc">https://emotiv.com/epoc</a>. Accessed online 14/08/2019. [2] Y. Renard, F. Lotte, G. Gibert, M. Congedo, E. Maby, V. Delannoy, O. Bertrand, A. L&eacute;cuyer. &ldquo;OpenViBE: An Open-Source Software Platform to Design, Test and Use Brain-Computer Interfaces in Real and Virtual Environments&rdquo;, Presence: teleoperators and virtual environments, vol. 19, no 1, 2010. [3] S. Zhao, F. Rudzicz. &quot;Classifying phonological categories in imagined and articulated speech.&quot; In Proceedings of ICASSP 2015, Brisbane Australia, 2015.</pre>

openodc-byNov 2019View details →
zenodo28/100

SOCIAL AND HOUSEHOLD FORMS OF EPIDEICTIC SPEECH GENRES IN THE UZBEK LANGUAGE (IN THE EXAMPLE OF COLLECTIVE PRAYERS)

Open the record for dataset details and reuse information.

opencc-by-4.0Aug 2024View details →
zenodo28/100

Dataset for "Capturing Formality in Speech Across Domains and Languages"

<p>We share the data used in our paper "Capturing Formality in Speech Across Domains and Languages", previously hosted on Google Drive. Corpora available in this dataset release include:</p> <ol> <li>All-India Radio (Hindi) [<a href="../api/records/13298510/draft/files/all_india_radio-20240812T152206Z-001.zip/content" target="_blank" rel="noopener noreferrer">all_india_radio-20240812T152206Z-001.zip</a>]</li> <li>Bangor Miami (Spanish-English) [<a href="../api/records/13298510/draft/files/bangor_miami_clean-20240812T152204Z-001.zip/content" target="_blank" rel="noopener noreferrer">bangor_miami_clean-20240812T152204Z-001.zip</a>]</li> <li>CallHome (English; Spanish) [<a href="../api/records/13298510/draft/files/callhome-20240812T152201Z-001.zip/content" target="_blank" rel="noopener noreferrer">callhome-20240812T152201Z-001.zip</a>; <a href="../api/records/13298510/draft/files/callhome-20240812T152201Z-002.zip/content" target="_blank" rel="noopener noreferrer">callhome-20240812T152201Z-002.zip</a>]</li> <li>CALLFriend (Hindi) [<a href="../api/records/13298510/draft/files/cf_hindi-20240812T152159Z-001.zip/content" target="_blank" rel="noopener noreferrer">cf_hindi-20240812T152159Z-001.zip</a>]</li> <li>HUB4-SE (Spanish) [<a href="../api/records/13298510/draft/files/hub4_se-20240812T152156Z-001.zip/content" target="_blank" rel="noopener noreferrer">hub4_se-20240812T152156Z-001.zip</a>]</li> <li>HUB5 (Mandarin) [<a href="../api/records/13298510/draft/files/hub5_transcript-20240812T152154Z-001.zip/content" target="_blank" rel="noopener noreferrer">hub5_transcript-20240812T152154Z-001.zip</a>]</li> <li>Multilingual TEDx (English; Spanish) [<a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-001.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-001.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-002.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-002.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-003.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-003.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-004.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-004.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-005.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-005.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-006.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-006.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-007.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-007.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-008.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-008.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-009.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-009.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-010.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-010.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-011.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-011.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-012.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-012.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-013.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-013.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-014.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-014.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-015.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-015.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-016.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-016.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-017.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-017.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-018.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-018.zip</a>, <a href="../api/records/13298510/draft/files/mtedx_es-en-20240812T152152Z-019.zip/content" target="_blank" rel="noopener noreferrer">mtedx_es-en-20240812T152152Z-019.zip</a>]</li> <li>Multitarget TED (English; Mandarin) [<a href="../api/records/13298510/draft/files/multitarget-ted-20240812T152149Z-001.zip/content" target="_blank" rel="noopener noreferrer">multitarget-ted-20240812T152149Z-001.zip</a>]</li> <li>IIT-B (Hindi) [<a href="../api/records/13298510/draft/files/parallel-n-20240812T042826Z-001.zip/content" target="_blank" rel="noopener noreferrer">parallel-n-20240812T042826Z-001.zip</a>]</li> <li>TDT4 (English; Mandarin) [<a href="../api/records/13298510/draft/files/tdt4_multilingual_news-20240812T152131Z-001.zip/content" target="_blank" rel="noopener noreferrer">tdt4_multilingual_news-20240812T152131Z-001.zip</a>]</li> <li>TED Talks India (Hindi) [<a href="../api/records/13298510/draft/files/ted_talks_hindi-20240812T042346Z-001.zip/content" target="_blank" rel="noopener noreferrer">ted_talks_hindi-20240812T042346Z-001.zip</a>]</li> <li>UN (Mandarin) [<a href="../api/records/13298510/draft/files/UNv1.0.en-zh-002.en.zip/content" target="_blank" rel="noopener noreferrer">UNv1.0.en-zh-002.en.zip</a>; <a href="../api/records/13298510/draft/files/UN-20240812T040956Z-002.zip/content" target="_blank" rel="noopener noreferrer">UN-20240812T040956Z-002.zip</a>; <a href="../api/records/13298510/draft/files/UN-20240812T040956Z-003.zip/content" target="_blank" rel="noopener noreferrer">UN-20240812T040956Z-003.zip</a>]</li> <li>YouTube (English; Spanish; Hindi; Mandarin) [<a href="../api/records/13298510/draft/files/youtube-20240812T040730Z-001.zip/content" target="_blank" rel="noopener noreferrer">youtube-20240812T040730Z-001.zip</a>; <a href="../api/records/13298510/draft/files/youtube-20240812T040730Z-002.zip/content" target="_blank" rel="noopener noreferrer">youtube-20240812T040730Z-002.zip</a>;&nbsp;<a href="../api/records/13298510/draft/files/youtube-20240812T040730Z-003.zip/content" target="_blank" rel="noopener noreferrer">youtube-20240812T040730Z-003.zip</a>]</li> <li>All-CS (Hindi-English) [<a href="../api/records/13298510/draft/files/All-CS.json/content" target="_blank" rel="noopener noreferrer">All-CS.json</a>]</li> <li>Europarl v7 (Spanish) [<a href="../api/records/13298510/draft/files/europarl-v7.es-en.es/content" target="_blank" rel="noopener noreferrer">europarl-v7.es-en.es</a>]</li> </ol> <p>If using our YouTube and/or TED Talks India corpora, please cite our paper:</p> <p>Bhattacharya, D., Chi, J., Hirschberg, J., Bell, P. (2023) Capturing Formality in Speech Across Domains and Languages. Proc. INTERSPEECH 2023, 1030-1034, doi: 10.21437/Interspeech.2023-1852</p>

restrictedcc-by-4.0Aug 2023View details →
zenodo28/100

THE REALIZATION OF TYPES OF SPEECH ACT AT LANGUAGE LEVELS

Open the record for dataset details and reuse information.

opencc-by-4.0Aug 2024View details →
zenodo28/100

FUNDAMENTALS OF SPEECH CULTURE AND ORATORY SKILLS

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →
zenodo28/100

New Outlooks on Backdoor Attacks for Fake Speech Detection

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →
zenodo28/100

STRATEGIES FOR IMPROVING ORAL SPEECH SKILLS IN ENGLISH

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2024View details →
dryad28/100

Data from: Contributions of local speech encoding and functional connectivity to audio-visual speech perception

Seeing a speaker's face enhances speech intelligibility in adverse environments. We investigated the underlying network mechanisms by quantifying local speech representations and directed connectivity in MEG data obtained while human participants listened to speech of varying acoustic SNR and visual context. During high acoustic SNR speech encoding by temporally entrained brain activity was strong in temporal and inferior frontal cortex, while during low SNR strong entrainment emerged in premotor and superior frontal cortex. These changes in local encoding were accompanied by changes in directed connectivity along the ventral stream and the auditory-premotor axis. Importantly, the behavioral benefit arising from seeing the speaker's face was not predicted by changes in local encoding but rather by enhanced functional connectivity between temporal and inferior frontal cortex. Our results demonstrate a role of auditory-frontal interactions in visual speech representations and suggest that functional connectivity along the ventral pathway facilitates speech comprehension in multisensory environments.

opencc-zeroDec 2016View details →
dryad28/100

Data from: Perceptually relevant speech tracking in auditory and motor cortex reflects distinct linguistic features

During online speech processing, our brain tracks the acoustic fluctuations in speech at different timescales. Previous research has focused on generic timescales (for example, delta or theta bands) that are assumed to map onto linguistic features such as prosody or syllables. However, given the high intersubject variability in speaking patterns, such a generic association between the timescales of brain activity and speech properties can be ambiguous. Here, we analyse speech tracking in source-localised magnetoencephalographic data by directly focusing on timescales extracted from statistical regularities in our speech material. This revealed widespread significant tracking at the timescales of phrases (0.6–1.3 Hz), words (1.8–3 Hz), syllables (2.8–4.8 Hz), and phonemes (8–12.4 Hz). Importantly, when examining its perceptual relevance, we found stronger tracking for correctly comprehended trials in the left premotor (PM) cortex at the phrasal scale as well as in left middle temporal cortex at the word scale. Control analyses using generic bands confirmed that these effects were specific to the speech regularities in our stimuli. Furthermore, we found that the phase at the phrasal timescale coupled to power at beta frequency (13–30 Hz) in motor areas. This cross-frequency coupling presumably reflects top-down temporal prediction in ongoing speech perception. Together, our results reveal specific functional and perceptually relevant roles of distinct tracking and cross-frequency processes along the auditory–motor pathway.

opencc-zeroDec 2017View details →
zenodo28/100

Subtitled speech: Phenomenology of tickertape synesthesia

<p>Instropective description of ticker-tape synesthesia, and description of their perception when listening to noises, pseudowords and other stimuli.</p> <p>Here are included: the dataset of questionnaire answers, the audio stimuli (noises, including humann, animal and noises), and&nbsp;the english version of the questionnaire.</p>

opencc-by-4.0Sep 2022View details →
zenodo28/100

Morgan Emotional Speech Set

<p>This database of emotional speech has been validated for use in experiments of auditory perception of emotion. Category ratings and emotional dimension ratings of activation and pleasantness&nbsp;are available from the researcher upon request.</p> <p>When used academically, please cite using the following citation:</p> <p>Morgan, S. D. (2019). Categorical and Dimensional Ratings of Emotional Speech: Behavioral Findings From the Morgan Emotional Speech Set. Journal of Speech, Language, and Hearing Research, 62(11), 4015-4029.</p> <p>&nbsp;</p>

opencc-by-4.0May 2019View details →
zenodo28/100

Aster: Automatic Speech Recognition System Accessibility Testing for Stutterers

<p>Test Cases for&nbsp;Aster: Automatic Speech Recognition System Accessibility Testing for Stutterers</p>

opencc-by-4.0May 2023View details →
zenodo28/100

Punjabi Dialect speeches

<p>Contains audio files recorded for Punjabi dialects.</p> <p>html file has the associated code for models.</p>

opencc-by-4.0Jun 2023View details →
zenodo28/100

Listening test data for "Investigating Range-Equalizing Bias in Mean Opinion Score Ratings of Synthesized Speech"

<p>This is the listening test data for the paper published at Interspeech 2023:<br>"Investigating Range-Equalizing Bias in Mean Opinion Score Ratings of Synthesized Speech"<br>Erica Cooper and Junichi Yamagishi<br>doi: 10.21437/Interspeech.2023-1076</p><p>Please cite this paper if you use this data in your work.</p><p>The audio data used in this study comes from past editions of the Blizzard Challenge, Voice Conversion Challenge, and published samples from ESPnet-TTS. &nbsp;Audio samples are not included in this dataset but instructions for obtaining them are included.</p>

openodc-byOct 2023View details →
ClinicalTrials.gov28/100

Speech Accessibility Project

ClinicalTrials.gov study NCT05889260. IPD Sharing: YES. Countries: 1. Publications: 0.

controlledIPD-YESFeb 2026View details →
ClinicalTrials.gov28/100

CP1150 Sound Processor Speech Perception Compared With the Next Generation of Signal Processing Technology

ClinicalTrials.gov study NCT05286385. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov28/100

Benefits of Assistive Listening Device for Speech Intelligibility

ClinicalTrials.gov study NCT05072470. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov28/100

Auditory Training With Speech Related Acoustic Cues Using Psychophysical Testing

ClinicalTrials.gov study NCT03867032. IPD Sharing: NO. Countries: 0. Publications: 18.

closedIPD-NOFeb 2026View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated datasets

Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record