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5 results for “Speech augmentation”
Creating speech zones with self-distributing acoustic swarms (Augmented Dataset Part 1 of 2)
<p>Datasets used in the paper: "Creating speech zones with self-distributing acoustic swarms"</p> <p>This deposit contains the <strong>first</strong> part of the augmented dataset containing simulated and real world collected data. The datasets contains 18000 training mixtures of 3-5 speakers, of which 6000 are simulated using PyRoomAcoustics, 6000 are created from synchronized real world recordings in an anechoic chamber, and 6000 are created from synchronized recordings in ordinary reverberant rooms.</p> <p>It also includes a validation set of 500 mixtures from reverberant rooms, and a testing set of 1000 mixtures from reverberant rooms.</p> <p>The source sounds are various utterances from the VCTK dataset. For real world data, the utterances are played over a Rokono Bass+ Mini Speaker. The recordings are captured from an array of 7 microphones, as they are recorded by our robotic swarm as it is distributed across the table. The recorded audio in the real world has been subjected to audio compression and decompression using the Opus Codec to enable multiple simultaneous streams.</p> <p>You must download <strong>both</strong> the first and the second part of this dataset in order to use it properly.</p> <p>To uncompress the two datasets, download both and execute:</p> <p>```cat *.tar.gz.* | tar xvfz -```</p> <p>Please see the Readme for more information. Please see related identifiers for other datasets.</p>
Creating speech zones with self-distributing acoustic swarms (Augmented Dataset Part 2 of 2)
<p>Datasets used in the paper: "Creating speech zones with self-distributing acoustic swarms"</p> <p>This deposit contains the <strong>second</strong> part of the augmented dataset containing simulated and real world collected data. The datasets contains 18000 training mixtures of 3-5 speakers, of which 6000 are simulated using PyRoomAcoustics, 6000 are created from synchronized real world recordings in an anechoic chamber, and 6000 are created from synchronized recordings in ordinary reverberant rooms.</p> <p>It also includes a validation set of 500 mixtures from reverberant rooms, and a testing set of 1000 mixtures from reverberant rooms.</p> <p>The source sounds are various utterances from the VCTK dataset. For real world data, the utterances are played over a Rokono Bass+ Mini Speaker. The recordings are captured from an array of 7 microphones, as they are recorded by our robotic swarm as it is distributed across the table. The recorded audio in the real world has been subjected to audio compression and decompression using the Opus Codec to enable multiple simultaneous streams.</p> <p>You must download <strong>both</strong> the first and the second part of this dataset in order to use it properly.</p> <p>To uncompress the two datasets, download both and execute:</p> <p>```cat *.tar.gz.* | tar xvfz -```</p> <p>Please see the Readme for more information. Please see related identifiers for other datasets.</p>
CpAug: Refining Copy-Paste Augmentation for Speech Anti-Spoofing
<p>Conventional copy-paste augmentations generate new training instances by concatenating existing utterances to increase the amount of data for neural network training. However, the direct application of copy-paste augmentation for anti-spoofing is problematic. This paper refines the copy-paste augmentation for speech anti-spoofing, dubbed CpAug, to generate more training data with rich intra-class diversity. The CpAug employs two policies: concatenation to merge utterances with identical labels, and substitution to replace segments in an anchor utterance. Besides, considering the impacts of speakers and spoofing attack types, we craft four blending strategies for the CpAug. Furthermore, we explore how CpAug complements the Rawboost augmentation method. Experimental results reveal that the proposed CpAug significantly improves the performance of speech anti-spoofing. Particularly, CpAug with substitution policy leads to relative improvements of 43% and 38% on the ASVspoof’ 19LA and 21LA, respectively. Notably, the CpAug and Rawboost synergize effectively, achieving an EER of 2.91% on ASVspoof’ 21LA.</p>
Speech Production Enhancement Using Augmentative Communication for Kids
ClinicalTrials.gov study NCT07173049. IPD Sharing: YES. Countries: 1. Publications: 6.
Noise-augmented Automatic Speech Recognition for Speech Treatment in Parkinson's Disease
ClinicalTrials.gov study NCT06540989. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.