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503 results for “Voice”
PÂTÉ: A Corpus of Temporal Expressions for the In-car Voice Assistant Domain
<p><strong>PÂTÉ: A Corpus of Temporal Expressions for the In-car Voice Assistant Domain</strong></p> <p>We present <strong>PÂTÉ: A Corpus of Temporal Expressions for the In-car Voice Assistant Domain</strong>. PÂTÉ is a dataset of natural-language commands containing temporal expressions for an AI voice assistant.</p> <p>The dataset is presented in:</p> <p>Zarcone, Alessandra and Alam, Touhidul and Kolagar, Zahra (2020). "PÂTÉ: A Corpus of Temporal Expressions for the In-car Voice Assistant Domain". <em>Proceedings of the 12th Conference on Language Resources and Evaluation (LREC)</em>. Marseille, France, May 11-16, 2020.</p> <p>The dataset is annotated according to the TimeML/TIMEX3 annotation guidelines (Saurı et al., 2006; TimeML Working Group, 2009). In our paper we also describe how we annotated cases which were not covered by the guidelines. We also provide an annotation of the Snips dataset (Coucke et al., 2018) following the same guidelines. </p> <p>Please refer to the README.md file inside PATE.zip for details about the files. </p>
Data from: Evaluation of the severity of major depression using a voice index for emotional arousal
<p>We first developed an arousal level voice index (ALVI) to measure arousal levels using the Interactive Emotional Dyadic Motion Capture database. Then, we calculated ALVI from the voices of depressed patients from two hospitals H1 and H2 and compared them with the severity of depression as measured by the Hamilton Rating Scale for Depression (HAM-D). Depending on the HAM-D score, the datasets were classified into a no depression (HAM-D<8) and a depression group (HAM-D≥8) for each hospital. A comparison of the mean ALVI between the groups was performed using the Wilcoxon rank-sum test.</p>
Identifying Improvement Opportunities in Software Engineering Education at the Maranhão State: Listening to Voices from Academy and Industry
<p>The teaching of Software Engineering (SE) has become challenging due to the large amount of content taught and the constant evolution of the software industry, directly impacting the requirements necessary to apply for a position in the area of Information Technology (IT). In this context, it is clear that students of computer courses still find it difficult to know how to prepare for the job market, and Higher Education Institutions (HEIs) may have difficulties in aligning themselves with market expectations with their syllabus in SE. This paper aims to provide a holistic view of the teaching-learning process, involving the views of students, HEIs and IT companies in the context of the state of Maranhão; and to propose reflections on the current state of the education to complement the contents and methodologies applied in the classroom with the needs of the local industry. To do so, three activities were carried out: (1) mapping expectations and evaluating higher education from the students’ point of view; (2) analysis of higher education disciplines in higher education institutions in the state of Maranhão in Brazil; and (3) survey of expectations of the IT market considering the vacancies disclosed in relation to the SE area in the state of Maranhão. The results indicate a partial fulfillment of the demand regarding the taught topics related to SE from the local industry by the HEIs. In addition, there is a need to improve the teaching-learning methodological processes, focusing on practical activities and the learning of teamwork skills, in addition to knowledge in software development.</p>
The Impact of Voice Leading and Harmony on Musical Expectancy
<p>This dataset contains the stimuli (as wav files) and the anonymized experimental data of the publication:</p> <p>Wall, Leona, Robert Lieck, Markus Neuwirth, and Martin Rohrmeier. 2020. “The Impact of Voice Leading and Harmony on Musical Expectancy.” <em>Scientific Reports</em> 10 (1). <a href="https://doi.org/10.1038/s41598-020-61645-4">https://doi.org/10.1038/s41598-020-61645-4</a>.</p>
Data from: More than just two sexes: the neural correlates of voice gender perception in gender dysphoria
Gender dysphoria (also known as "transsexualism") is characterized as a discrepancy between anatomical sex and gender identity. Research points towards neurobiological influences. Due to the sexually dimorphic characteristics of the human voice, voice gender perception provides a biologically relevant function, e.g. in the context of mating selection. There is evidence for a better recognition of voices of the opposite sex and a differentiation of the sexes in its underlying functional cerebral correlates, namely the prefrontal and middle temporal areas. This fMRI study investigated the neural correlates of voice gender perception in 32 male-to-female gender dysphoric individuals (MtFs) compared to 20 non-gender dysphoric men and 19 non-gender dysphoric women. Participants indicated the sex of 240 voice stimuli modified in semitone steps in the direction to the other gender. Compared to men and women, MtFs showed differences in a neural network including the medial prefrontal gyrus, the insula, and the precuneus when responding to male vs. female voices. With increased voice morphing men recruited more prefrontal areas compared to women and MtFs, while MtFs revealed a pattern more similar to women. On a behavioral and neuronal level, our results support the feeling of MtFs reporting they cannot identify with their assigned sex.
Voice localizer short
<p>252 vocal and 252 nonvocal 500-ms long stimuli , and their phase-scrambled controls.</p>
ESC-50-Voice: Dataset of vocal imitation for environmental sound in ESC-50
<h2><strong>Description</strong></h2> <p>This is a dataset with vocal imitation, which involve the process of replicating or mimicking the rhythm and pitch of sounds by voice for an environmental sound in ESC-50 [1] that can be used in various tasks that use environmental sounds. The dataset consists of 9,920 vocal imitations (8 imitators per environmental sound). Each imitator is a Japanese speaker. All audio data are 48kHz/16bit wav files. </p> <p>Each audio file is named as follows:</p> <pre><code>vocal_imitation/SpeakerID/FileName_SpeakerID.wav</code></pre> <p>FileName means the original audio file name in ESC-50. SpaekerID means the ID of the imitator. We recorded vocal imitations for a part of sound events in ESC-50. A list of the sound events used can be obtained from EventList.csv.</p> <p>Note that this dataset does not contain environmental sound files, which can be obtained from ESC-50. Environmental sounds in ESC-50 are available <a href="https://github.com/karolpiczak/ESC-50">here</a>.</p> <h2><strong>Terms of use</strong></h2> <p>The materials may be used free of charge for research purposes, but please refrain from redistribution or use that is offensive to public order and morals. If you want to use for commercial purposes, please contact us (Yuki Okamoto or Keisuke Imoto).</p> <h2><strong>Citation</strong></h2> <p>If you use this dataset, please cite as follow:</p> <p>Yuki Okamoto, Keisuke Imoto, Shinnosuke Takamichi, Ryotaro Nagase, Takahiro Fukumori, and Yoichi Yamashita, "Environmental Sound Synthesis from Vocal Imitations and Sound Event Labels," Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 411-415, 2024.</p> <h2><strong>Feedback</strong></h2> <p>If there is any problem, please contact us</p> <ul> <li>Yuki Okamoto, <a href="mailto:y-okamoto@ieee.org">y-okamoto@ieee.org</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> </ul> <p> </p> <p>[1] K. J. Piczak, "Esc: Dataset for environmental sound classification,” in Proc. the 23rd ACM International Conference on Multimedia, 2015, p. 1015–1018.</p>
Raw data used in Akshay Madhav Deshmukh and Ricardo Chalmeta 2025 DEVELOPMENT AND VALIDATION OF A USER EXPERIENCE EVALUATION QUESTIONNAIRE FOR VOICE USER INTERFACES
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Chord voicings for 370 chorales by J. S. Bach as generated by the 'voicer' algorithm
<p>This is a dataset of 370 chorales by J S. Bach as generated by the 'voicer' algorithm for automatic chord voicing. This dataset is intended to accompany the paper 'A Computational Model for the Analysis and Generation of Chord Voicings'.</p> <p>Note: there are 370 chorales, but the numbering goes from 1 to 371, with one chorale omitted, matching the original dataset from http://kern.humdrum.org.</p>
Hearing the Voice of Software Practitioners on Technical Debt Monitoring: Understanding Monitoring Practices and the Practices' Avoidance Reasons: Data set
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Recordings of classical music (voice with piano, piano solo) with smartphones and professional audio equipment
<p>To investigate differences of performance rating and perception of classical music demo videos, two demos were recorded with six devices (four Smartphones with main cam video function, one field recorder, one professional setup). Video and Audio were separated for each file, only the audio files were used in the study and are thus presented here. The music of the voice demo is from the genres of romantic lied and romantic and modern opera, the piano solo music was composed in the 20th century.</p> <p>The documentation follows the recommendations of the German Society for Acoustics (DEGA), the information is as following:</p> <p>A 3D model of the recording situation.<br>The audio files, normalised (as used in the corresponding study) and with original level.<br>Geometric measurements of room dimensions.<br>Pictures of the recording.<br>A list of the equipment and recording system.<br>Render statistics (provided by the DAW used, Reaper).<br>Scores of two of the three pieces.<br>A Takelist.</p>
Human fMRI data - Comparison of Auditory fMRI Protocols for a Voice Localizer
<p>Dataset for the study "Comparison of Auditory fMRI Protocols for a Voice Localizer"</p>
Figure 9 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 9 - Timeline. We will prepare and update the Web app (Aim 2) as we develop it for use in annotating gold standard audio data (Aim 1) and as we get feedback on its use in connection with Amazon's Mechanical Turk (Aim 2). Year 2 will consist primarily of testing the aggregation of annotated audio data for further analysis (Aim 2), to train an automated approach (Exploratory Aim), and to publish and present our findings.
Figure 2 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 2 - Mockup of audio recording annotation tool – Step 1: Selection. This figure shows a mockup of what an audio annotation Web application tool could look like. In this first step, (A) the Worker presses the Play icon to listen to the voice recording, (B) selects a problematic segment by clicking and dragging the mouse over the waveform, and (C) replays the recording if necessary and selects other problematic segments.
Figure 5 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 5 - DARPA-funded seedling project. This schematic represents our DARPA-funded seedling project to assess the feasibility of collecting phone voice recordings from PD patients for use in a competition.
Figure 4 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 4 - Audio recording annotation tool – Step 3: Rating. Following Figures 2 and 3, here the Worker rates how serious the problem is that is affecting the highlighted segment of the recording. In this example, the Worker indicates that the background noise (wind) is not good, but that it doesn't interfere with his/her ability to hear the voice in the recording.
Figure 7 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 7 - Example mPower patient voice data. In the mPower app, PD patients are prompted to perform the voice activity three times per day: once before taking their medication, a second time when they feel they are at their best after taking their medication, and a third "random" time. This figure shows example voice data for a single patient on medication (top) and at a "random" time, very likely off medication (bottom). On the left are waveforms, showing the acoustic voice signal over time (0-10 seconds), from which one can clearly see that the patient's voice trailed off to a minimum (bottom left) compared to after medication (top left). On the right are spectrograms, representing signal amplitude at different frequencies (0-5 kHz) over time (0-10 seconds). The spectrogram after medication (top right) has more uniform frequency bands across the recording compared to the rather "muddled" spectrogram recorded at the random time (bottom right).
Figure 3 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 3 - Audio recording annotation tool – Step 2: Annotation. Following Figure 2, here the Worker selects one or more categories describing why the highlighted segment in the audio waveform is problematic. In this example, there was a lot of background noise (wind).
Figure 6 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 6 - Android and iOS Parkinson app screenshots. Top: Android PD app screenshots showing instructions for the phonation (voice) task. Bottom: mPower PD app screenshots. Each participant in the mPower study is prompted to perform a voice activity three times a day. The rightmost screenshot demonstrates the visual feedback that is provided during audio recording, to try to keep the voice at the best amplitude for recording.
HANNA Voice Commands Dataset
<p>This dataset consists of recordings for device provisioning and configuration recorded from 10 different speakers, 5 female, 5 male with different levels of English language proficiency. The recording are also mixed with noise corresponding to factory, office, talking and traffic backgrounds to emulate corresponding environments. It was used to evaluate the performance of various speech to text engines.</p>
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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.
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.
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.
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.
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.