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28 results for “Speech Processing”
EEG Data for: "Cortical oscillations and entrainment in speech processing during working memory load"
<p>This repository contains EEG and audio data used and described in:</p> <p><strong>Hjortkjær, J, Märcher-Rørsted, J, Fuglsang, SA, Dau, T (2018). Cortical oscillations and entrainment in speech processing during working memory load. European Journal of Neuroscience. </strong><strong>doi</strong><strong>:10.1111/ejn.13855</strong></p> <p>Please cite this article when using the data</p> <p> </p> <p>The MAT-files contain the aligned EEG and audio data for each subject (N=22). The envelopes of the speech audio (without noise) have been extracted as described in the paper. Each file (data_N.mat) contains a Matlab struct in the format of the Fieldtrip toolbox containing the following fields:</p> <p> </p> <p>data.trial: EEG and audio data for all 40 trials [channels x timepoints]</p> <ul> <li>channels 1-64: scalp EEG</li> <li>channel 65: left mastoid electrode</li> <li>channel 66: right mastoid electrode</li> <li>channel 67: horizontal EOG</li> <li>channel 68: vertical EOG for left eye</li> <li>channel 69: vertical EOG for right eye</li> <li>channel 70: audio envelopes</li> </ul> <p>data.trialinfo: Experimental condition in each trial</p> <ul> <li>1 = low noise, 1-back</li> <li>2 = low noise, 2-back</li> <li>3 = high noise, 1-back</li> <li>4 = high noise, 2-back</li> </ul> <p>data.time: Sample indices for each trial in seconds</p> <p>data.label: Name of each channel in data.trial</p> <p>data.fsample: EEG/audio sampling rate in Hz (128)</p>
Speech and noise mixtures used in Modelling Auditory Processing and Organisation
<p>Speech and noise signals used in Cooke, M (1991) Modelling Auditory Processing and Organisation, Ph. D. Thesis, Department of Computer Science, University of Sheffield</p>
Effects of PSAPs on Speech Processing
ClinicalTrials.gov study NCT05076045. IPD Sharing: YES. Countries: 1. Publications: 2.
The data and code for "Original Speech and Its Echo are Segregated and Separately Processed in the Human Brain"
<p>This dataset is associated with the manuscript "Original Speech and Its Echo are Segregated and Seperately Processed in the Human Brain", and provides the preprocessed MEG response (resampling to 100 Hz), auditory stimulus, individual quantitative observations underlying the data summarized in figures, and the analysis codes.</p>
VoiceHome-2 corpus : A corpus dedicated to distant-microphone speech processing in domestic environments
<p><strong>Purpose: </strong></p> <p>This corpus includes reverberated, noisy speech signals spoken by 12 native French talkers in 4 houses (3 rooms per house) and recorded by an 8-microphone device at various angles and distances and in various noise conditions.</p> <p>This corpus stands apart from other corpora in the field by the number of rooms and homes considered by the diversity of acoustic conditions recorded and by the facts that it is publicly available at no cost.</p> <p><strong>Other materials:</strong></p> <ul> <li>Article : N. Bertin, E. Camberlein, R. Lebarbenchon, E. Vincent, S. Sivasankaran, I. Illina and F. Bimbot: <a href="https://hal.inria.fr/hal-01923108"><strong>VoiceHome-2, an extended corpus for multichannel speech processing in real homes</strong></a>, <em>Speech Communication</em>, Elsevier : North-Holland, 2019, 106, pp.68-78. <a href="https://dx.doi.org/10.1016/j.specom.2018.11.002">⟨10.1016/j.specom.2018.11.002⟩</a>.</li> <li>Code baseline to reproduce article's results: <ul> <li><a href="https://hal.inria.fr/hal-02963528">Localization and speech enhancement</a></li> <li><a href="https://doi.org/10.5281/zenodo.4079314">Acoustic models</a> and <a href="https://hal.inria.fr/hal-02963802">recognition scripts</a> for automatic speech recognition</li> </ul> </li> <li>Related software to execute the baseline: <ul> <li><a href="https://gitlab.inria.fr/bass-db/mbss_locate">MBSS Locate (v2.0)</a></li> <li><a href="https://gitlab.inria.fr/bass-db/fasst">FASST</a></li> </ul> </li> </ul> <p><strong>Documentation:</strong></p> <p>The corpus documentation is both available into the archive and hereafter by clicking on voiceHome-2_corpus_v1.0_documentation.pdf .</p> <p><strong>Terms of use</strong></p> <p>You may exploit the corpus for a non-commercial scientific purpose provided you mention it in any written work or software you derive from its use. Within a published article, paper or report, the corpus must appear in the bibliographical references.</p> <p><strong>Speaker records diffusion consent</strong></p> <p>All participants have given an informed and signed consent about public diffusion of recorded sentences.</p> <p><strong>Contact:</strong></p> <p>nancy [dot] bertin [at] irisa [dot] fr</p>
voiceHome corpus: A corpus dedicated to distant-microphone speech processing in domestic environments
<p><strong>Purpose: </strong></p> <p>This corpus includes reverberated, noisy speech signals spoken by native French talkers in a lounge and recorded by an 8-microphone device at various angles and distances and in various noise conditions.</p> <p>Room impulse responses and noise-only signals recorded in various real rooms and homes and baseline speaker localization and enhancement software are also provided.</p> <p>This corpus stands apart from other corpora in the field by the number of rooms and homes considered and by the fact that it is publicly available at no cost.</p> <p> </p> <p><strong>Other materials:</strong></p> <ul> <li>Article: N. Bertin, E. Camberlein, E. Vincent, R. Lebarbenchon, S. Peillon, E. Lamandé, S. Sivasankaran, F. Bimbot, I. Illina, A. Tom, S. Fleury and E. Jamet: <a href="https://hal.inria.fr/hal-01343060"><strong>A French corpus for distant-microphone speech processing in real homes</strong></a>, Interspeech2016, Sep 2016, San Francisco, United States, 2016.</li> <li>Related software to reproduce article's results: <ul> <li><a href="https://gitlab.inria.fr/bass-db/mbss_locate">Multi-Channel BSS Locate (v1.3)</a></li> <li><a href="https://gitlab.inria.fr/bass-db/fasst">FASST (v2.2.1)</a></li> </ul> </li> </ul> <p><strong>Documentation (in french):</strong></p> <p>The corpus documentation is both available into the archive and hereafter by clicking on voiceHome_corpus_french_documentation_v1.2.pdf .</p> <p><strong>Terms of use</strong></p> <p>You may exploit the corpus for a non-commercial scientific purpose provided you mention it in any written work or software you derive from its use. Within a published article, paper or report, the corpus must appear in the bibliographical references.</p> <p><strong>Speaker records diffusion consent</strong></p> <p>All participants have given an informed and signed consent about public diffusion of recorded sentences.</p> <p><strong>New corpus version available : voiceHome-2 corpus</strong></p> <p>A new version of the corpus is available : <a href="https://doi.org/10.5281/zenodo.1252143"><strong>voiceHome-2 corpus web page</strong></a></p> <p> </p>
Data from: Neural dynamics of the processing of speech features: Evidence for a progression of features from acoustic to sentential processing
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THLS - An open source dataset for Brazilian Portuguese speech processing
<p>THLS Open Source Brazilian Portuguese Speech Dataset with 1000 sentences balanced phonetically.</p> <p> </p> <p>Authors:</p> <ul> <li>Luiz Felipe Vecchietti</li> <li>Thalles Melo Batista Pieroni (Voice)</li> </ul> <p> </p> <p>https://gitlab.com/lfelipesv/1000-sentences-thls-dataset</p>
Data from: Parallel processing in speech perception with local and global representations of linguistic context
<p>Speech processing is highly incremental. It is widely accepted that human listeners continuously use the linguistic context to anticipate upcoming concepts, words, and phonemes. However, previous evidence supports two seemingly contradictory models of how a predictive context is integrated with the bottom-up sensory input: Classic psycholinguistic paradigms suggest a two-stage process, in which acoustic input initially leads to local, context-independent representations, which are then quickly integrated with contextual constraints. This contrasts with the view that the brain constructs a single coherent, unified interpretation of the input, which fully integrates available information across representational hierarchies, and thus uses contextual constraints to modulate even the earliest sensory representations. To distinguish these hypotheses, we tested magnetoencephalography responses to continuous narrative speech for signatures of local and unified predictive models. Results provide evidence that listeners employ both types of models in parallel. Two local context models uniquely predict some part of early neural responses, one based on sublexical phoneme sequences, and one based on the phonemes in the current word alone; at the same time, even early responses to phonemes also reflect a unified model that incorporates sentence-level constraints to predict upcoming phonemes. Neural source localization places the anatomical origins of the different predictive models in nonidentical parts of the superior temporal lobes bilaterally, with the right hemisphere showing a relative preference for more local models. These results suggest that speech processing recruits both local and unified predictive models in parallel, reconciling previous disparate findings. Parallel models might make the perceptual system more robust, facilitate processing of unexpected inputs, and serve a function in language acquisition.</p>
Study of a Signal-processing Algorithm Aiming at Improving Speech-in-noise Intelligibility in Normal-hearing and Hearing-impaired Persons
ClinicalTrials.gov study NCT04775810. IPD Sharing: NO. Countries: 1. Publications: 17.
Characterization of Auditory Processing Involved in the Encoding of Speech Sounds
ClinicalTrials.gov study NCT02574299. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Role of Auditory Cortical Oscillations in Speech Processing and Dyslexia
ClinicalTrials.gov study NCT04277351. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Parallel processing in speech perception with local and global representations of linguistic context
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Data from: Long-term use benefits of personal frequency-modulated systems for speech in noise perception in patients with stroke with auditory processing deficits: a non-randomised controlled trial study
Objectives: Approximately one in five stroke survivors suffer from difficulties with speech reception in noise, despite normal audiometry. These deficits are treatable with personal Frequency Modulated systems (FMs). This study aimed to evaluate long term benefits in speech reception in noise, after daily 10 week use of personal FMs, in non-aphasic stroke patients with auditory processing deficits. Design: This was a prospective non randomised controlled trial study. Patients were allocated to an intervention care group or standard care subjects group according to their willingness to use the intervention or not. Setting: Tertiary care setting. Participants: Nine non-aphasic subjects with ischemic stroke, normal/near normal audiometry, and auditory processing deficits and with reported difficulties understanding speech in background noise were recruited in the subacute stroke stage (3-12 months after stroke). Interventions: Four patients (intervention care subjects) used the FMs in their daily life over 10 weeks. Five patients (standard care subjects) received standard care. Primary outcome measures: All subjects were tested at baseline (visit 1) and 10 weeks later (visit 2) on a sentences in noise test with the FMs (aided) and without the FMs (unaided). Results: Speech reception thresholds showed clinically and statistically significant improvements in intervention but not in standard care subjects at 10 weeks in both aided and unaided conditions. Conclusions: 10 week use of FM systems by adult stroke patients may lead to benefits in unaided speech in noise perception. Our findings may indicate auditory plasticity type changes and require further investigation.
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.
Evaluation of an Open-source Speech-processing Platform
ClinicalTrials.gov study NCT03686046. IPD Sharing: NO. Countries: 1. Publications: 4.
Testing a Possible Cause of Reduced Ability of Children to Process Speech in Noise
ClinicalTrials.gov study NCT00001957. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: Long-term use benefits of personal frequency-modulated systems for speech in noise perception in patients with stroke with auditory processing deficits: a non-randomised controlled trial study
Open the record for dataset details and reuse information.
New Algorithms to Signal Processing for Speech Enhancement in Adult Cochlear Implant Recipients.
ClinicalTrials.gov study NCT06100393. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Clear Speech on Listening Effort and Memory in Sentence Processing
ClinicalTrials.gov study NCT06053190. IPD Sharing: YES. Countries: 1. Publications: 0.
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