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13 results for “listening tests”
Listening test results for sound field synthesis localization experiment
<p>Result files from the the localization experiments described in section 5.1 of Wierstorf [1].</p> <p>The results are visually summarized in Fig. 5.4, see https://github.com/hagenw/phd-thesis/tree/master/05_psychoacoustics/fig5_04</p> <p>[1] H. Wierstorf, Perceptual Assessment of Sound Field Synthesis, PhD dissertation, TU Berlin, 2014.</p>
Listening Test Results
<p>A listening test was conducted to determine how well auralizations of aircraft match with recordings of aircraft. This dataset contains the results of the listening test.</p>
Voice Conversion Challenge 2020 Listening Test Data
<pre>Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform. In 2016, we have launched the Voice Conversion Challenge (VCC) 2016 [1][2] at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems. In 2018, we have launched the second edition of VCC, the VCC 2018 [3]. In the second edition, we revised three aspects of the challenge. First, we educed the amount of speech data used for the construction of participant's VC systems to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, we introduced a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. We then evaluate both parallel and non-parallel voice conversion systems via the same large-scale crowdsourcing listening test. Third, we also attempted to bridge the gap between the ASV and VC communities. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, we also asses spoofing performance of the VC systems based on anti-spoofing scores. In 2020, we launched the third edition of VCC, the VCC 2020 [4][5]. In this third edition, we constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. The dataset for intra-lingual VC consists of a smaller parallel corpus and a larger nonparallel corpus, where both of them are of the same language. The dataset for cross-lingual VC consists of a corpus of the source speakers speaking in the source language and another corpus of the target speakers speaking in the target language. As a more challenging task than the previous ones, we focused on cross-lingual VC, in which the speaker identity is transformed between two speakers uttering different languages, which requires handling completely nonparallel training over different languages. As for listening test, we subcontracted the crowd-sourced perceptual evaluation with English and Japanese listeners to Lionbridge TechnologiesInc. and Koto Ltd., respectively. Given the extremely large costs required for the perceptual evaluation, we selected 5 utterances (E30001, E30002, E30003,E30004, E30005) only from each speaker of each team. To evaluate the speaker similarity of the cross-lingual task, we used audio in both the English language and in the target speaker’s L2language as reference. For each source-target speaker pair, we selected three English recordings and two L2 language recordings as the natural reference for the converted five utterances. </pre> <p>This data repository includes the audio files used for the crowd-sourced perceptual evaluation and raw listening test scores. </p> <pre>[1] Tomoki Toda, Ling-Hui Chen, Daisuke Saito, Fernando Villavicencio, Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "The Voice Conversion Challenge 2016" in Proc. of Interspeech, San Francisco. [2] Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "Analysis of the Voice Conversion Challenge 2016 Evaluation Results" in Proc. of Interspeech 2016. [3] Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhenhua Ling, "The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods", Proc Speaker Odyssey 2018, June 2018. [4] Yi Zhao, Wen-Chin Huang, Xiaohai Tian, Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Zhenhua Ling, and Tomoki Toda. "Voice conversion challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion" Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14. [5] Rohan Kumar Das, Tomi Kinnunen, Wen-Chin Huang, Zhenhua Ling, Junichi Yamagishi, Yi Zhao, Xiaohai Tian, and Tomoki Toda. "Predictions of subjective ratings and spoofing assessments of voice conversion challenge 2020 submissions." Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 99-120, DOI: 10.21437/VCC_BC.2020-15. </pre>
Listening test results for sound field synthesis localization experiment -- head movement data
<p>This data set contains recorded head movements listeners did during several localisation tasks in the context of sound field synthesis. This is an add-on to the actual localisation results provided by [1].</p> <p>[1] Wierstorf, H. (2016). Listening test results for sound field synthesis localization experiment [Data set]. Zenodo. http://doi.org/10.5281/zenodo.55439</p>
Listening test stimuli and tests
<p>A listening test was conducted to determine how well auralizations of aircraft match with recordings of aircraft. This dataset contains the listening tests and stimuli that were used. In a paired comparison participants were asked to rate how similar the two sounds sounded. The order of the stimuli was modified for each participant. Therefore, each `.html` indicates one listening test.</p>
Listening tests_Calleri et al.
<p>This audio files have been obtained through auralisations performed using Odeon (v.13) software. Results of the listening tests performed using such auralisations are presented in scientific works actually under review</p>
Dataset, test programs and analysis scripts for the related paper "Effects of reverberation on speech intelligibility in noise for hearing-impaired listeners"
<p>This dataset contains the data, test programs and analyses scripts used for a study submitted as a stage 2 registered report for Royal Society Open Science.</p>
Listen in: Developing and Testing a Therapy Application for Patients With Speech Comprehension Deficits After Stroke.
ClinicalTrials.gov study NCT02540889. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Will Listening to Music Make it Easier to Take a Visual Field Test?
ClinicalTrials.gov study NCT01027039. IPD Sharing: YES. Countries: 1. Publications: 1.
ASVspoof 2019 LA Listening Test Data for Partial Rank Similarity MOS Prediction
<p>This dataset is a derivitave work of the ASVSpoof 2019 LA condition listening test data found here:<br> https://datashare.ed.ac.uk/handle/10283/3336<br> -> LA.zip</p> <p>"ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech" <br> Xin Wang, Junichi Yamagishi, Massimiliano Todisco, Héctor Delgado, Andreas Nautsch, Nicholas Evans, Md Sahidullah, Ville Vestman, Tomi Kinnunen, Kong Aik Lee, Lauri Juvela, Paavo Alku, Yu-Huai Peng, Hsin-Te Hwang, Yu Tsao, Hsin-Min Wang, Sébastien Le Maguer, Markus Becker, Fergus Henderson, Rob Clark, Yu Zhang, Quan Wang, Ye Jia, Kai Onuma, Koji Mushika, Takashi Kaneda, Yuan Jiang, Li-Juan Liu, Yi-Chiao Wu, Wen-Chin Huang, Tomoki Toda, Kou Tanaka, Hirokazu Kameoka, Ingmar Steiner, Driss Matrouf, Jean-François Bonastre, Avashna Govender, Srikanth Ronanki, Jing-Xuan Zhang, Zhen-Hua Ling.<br> Computer Speech and Language Colume 64, 2020.</p> <p>This form of the data was used for the PRS paper accepted to ASRU 2023:</p> <p>"Partial Rank Similarity Minimization Method for Quality MOS Prediction of <br> Unseen Speech Synthesis Systems in Zero-shot and Semi-supervised Setting."<br> Hemant Yadav, Erica Cooper, Junichi Yamagishi, Sunayana Sitaram, Rajiv Ratn Shah.</p> <p>Modifications to the original data include converting audio from flac -> wav, sv56 normalization, conversion of labels from an 0-9 rating scale to a 1-5 scale, and creation of training/development/testing splits.</p>
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. Audio samples are not included in this dataset but instructions for obtaining them are included.</p>
Perceptual comparison between the auralized and the real room for a classroom scenario - listening test results
<p>The raw data of the listening test.</p>
Testing a Music Listening mHealth Intervention for Stress Reduction in Early Recovery (CalmiFy II)
ClinicalTrials.gov study NCT07088237. IPD Sharing: YES. Countries: 0. Publications: 0.
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