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28 results for “TTS”
WOLOF TTS(Text To Speech) Data
<p>This contains a WOLOF Text To Speech(TTS) dataset, it contains recordings from two natif Wolof actos (a male and female voice).<br> Each actor recored more than 20 000 sentences.<br> The notebook accompanying the dataset contains a brief analysis of the dataset and the code creating the appropriate train/validation and test set.<br> The file [male, female]train, [male, female]validation and [male, female]test are also present to extract the corespondig audios inside the data-commonvoice.zip</p> <p>The text dataset come from news website, Wikipedia and self curated text. We made sure with the help of our Wolof expert that the text dataset cover the different phonemes in the Wolof language.</p>
Speech/text alignments for Italian end-to-end TTS
<p>Here are 146 chapters of several audiobooks from Librivox (33:22:19.176) read by 33 italian speakers:</p> <ul> <li>19 Females<br>#LC (Lisa Caputo): 18 chapters - 8643 utterances> 07:51:25.991<br>#EG (Enrica Giampieretti): 16 chapters - 8473 utterances> 07:04:47.044<br>#MT (Mariateresa): 7 chapters - 2735 utterances> 02:57:0.487<br>#MR (Mariarosa): 2 chapters - 1150 utterances> 00:55:59.825<br>#FA (Fabiola) 2 chapters - 324 utterances> 00:13:36.635<br>#NI (Nicole Grassi) 2 chapters - 396 utterances> 00:19:26.222<br>#SP (Simona Pagliari) 7 chapters - 557 utterances> 00:26:58.920<br>#MM (Marzia Marianera) 2 chapters - 523 utterances> 00:28:18.365<br>#ANGE (Angelina) 2 chapters - 246 utterances> 00:15:45.223<br>#LAURA (Laura) 2 chapters - 899 utterances> 00:38:33.592<br>#FG (Filippo Gioachin): 4 chapters - 1358 utterances> 00:58:59.194<br>#GIOEMILY (?) 2 chapters - 743 utterances> 00:33:26.872<br>#CAPI (Silvia di Simone) 1 chapters - 482 utterances> 00:29:16.553<br>#ALLIE (Allie Cingi) 2 chapters - 838 utterances> 00:50:31.221<br>#CAIMMA () 1 chapters - 328 utterances> 00:19:26.483<br>#DOLCINEA () 1 chapters - 393 utterances> 00:19:19.275<br>#MGT (Maria Grazia Tundo) 1 chapters - 290 utterances> 00:10:41.887<br>#FR (Francesca Roma) 2 chapters - 246 utterances> 00:16:16.611<br>#PETULA () 3 chapters - 251 utterances> 00:16:27.894</li> <li>14 Males:<br>#RF (Riccardo Fasol): 3 chapters - 961 utterances> 01:03:59.329<br>#RC (Roberto Confini): 3 chapters - 1238 utterances> 00:42:2.761<br>#SB (Sergio Baldelli): 2 chapters - 910 utterances> 00:54:57.145<br>#DA (Daniele) 2 chapters - 420 utterances> 00:24:36.827<br>#RECL (Renzo Clerico) 3 chapters - 778 utterances> 00:37:31.304<br>#STRALF (?) 3 chapters - 1392 utterances> 00:51:23.847<br>#PAOLO (?) 2 chapters - 484 utterances> 00:34:3.345<br>#PIER (?) 1 chapters - 320 utterances> 00:14:10.860<br>#AB (Andrea Briglia) - 31 chapters - 863 utterances> 00:40:26.526<br>#SIRJOE (Sergio Bersanetti) 2 chapters - 626 utterances> 00:31:39.754<br>#KIUKKO (Luigi Chiaro) 1 chapters - 368 utterances> 00:17:48.204<br>#AZ (Francesco Montana) 1 chapters - 287 utterances> 00:14:39.814<br>#BM (Beniamino Massimo) 1 chapters - 415 utterances> 00:16:6.842<br>#ML (Mirko Lamberti) 1 chapters - 252 utterances> 00:12:27.594<br><br></li> <li>and a dictionnary of 14969 words with aligned phones</li> </ul> <p>Sources:</p> <ul> <li>Audios are from <a href="https://librivox.org/">Librivox</a></li> <li>Aligned original texts are from diverses sources including <a href="https://www.intratext.com">Intratext</a>, <a href="https://it.wikisource.org">wikisource</a>, <a href="https://www.pirandelloweb.com">pirandelloweb</a>, etc</li> </ul> <p>Each .wav file (sampled at 22050Hz) corresponds to one entire chapter. The format of the filenames is:<br>{author's acronym}_{book's acronym}_{reader's acronym}_{volume's number}_{chapter's number}</p> <p>The IT.csv file gives text (or sometimes, phones) and signal alignments for utterances in 4 fields separated by '|': {filename}|{start_ms}|{end_ms}|{text or phonetic content}. Most utterances are separated by at least a pause of 400ms (exceptionally less when phonation exceeds 11s). The intervals [start_ms:end_ms] comprise leading and trailing silences of 130ms (since wavs are entire chapters, these silences are "true" ambient silences).</p> <p>When phonetic alignment has been performed, 1 additional field has been added: {aligned phones}. Each input character or phone has a corresponding aligned phone. Note that all aligned utterances start and end with an aligned silence of 130ms. The set of aligned phones comprises:</p> <ul> <li>The set of input phones</li> <li>The silence: '__'</li> <li>The symbol '_' for silent characters, e.g. "occhi" is aligned with 'o^1 k: _ _ i'</li> </ul> <p>Text is in UTF8. '«»','—', '~','""','()','[]' are respectively used for speaking quotes, turn switches, three dots, quoted expression, aside quotes, notes. Because of rare occurrences, 'ö' has been transcribed as 'oe'. Paragraphs (two consecutive carriage returns in the original text) are cued by a special character '§'. It usually ends an utterance but could be used within an utterance if its associated pause is too short.</p> <p>Part of text under clear emphasis is surrounded by "#"</p> <p>When available, phonetic content is given per word in curly brackets '{}'. We use 39 phonetic symbols:</p> <ul> <li><strong>oral vowels</strong>: a (f<strong>a</strong>), e (v<strong>e</strong>), e^ (<strong>e</strong>d), i (r<strong>iz</strong>), u (t<strong>u</strong>), o (un<strong>o</strong>), o^ (c<strong>o</strong>n)</li> <li><strong>loan vowels & diphtongs: </strong>a&i and x^ (t<strong>i</strong>m<strong>er</strong>),</li> <li><strong>semi-vowels</strong>: h (g<strong>h</strong>etto.), w (q<strong>u</strong>el), j (va<strong>j</strong>)</li> <li><strong>consonants</strong>: p (vespa), t (<strong>t</strong>u), k (<strong>c</strong>alde), b (<strong>b</strong>uon), d (<strong>d</strong>isse), g (<strong>g</strong>razie), f (<strong>f</strong>ame), s (<strong>s</strong>auna) , s^ (<strong>sc</strong>ia), v (<strong>v</strong>erde), z (ro<strong>s</strong>a), z^ (<strong>j</strong>udo), r (<strong>r</strong>izo), l (<strong>l</strong>etto), l^ (e<strong>gl</strong>i), m (<strong>m</strong>apo), n (<strong>n</strong>uda), n~ (pu<strong>gn</strong>i)</li> <li><strong>long/double consonants are suffxed by ":",</strong> e.g. p: (zu<strong>pp</strong>a)</li> <li><strong>primary stress</strong> if any is noted "1" and appended to the vowel, e.g. a1 g a p e (agape)</li> </ul>
TIMIT-TTS: a Text-to-Speech Dataset for Synthetic Speech Detection
<p>With the rapid development of deep learning techniques, the generation and counterfeiting of multimedia material are becoming increasingly straightforward to perform. At the same time, sharing fake content on the web has become so simple that malicious users can create unpleasant situations with minimal effort. Also, forged media are getting more and more complex, with manipulated videos (e.g., deepfakes where both the visual and audio contents can be counterfeited) that are taking the scene over still images.<br> The multimedia forensic community has addressed the possible threats that this situation could imply by developing detectors that verify the authenticity of multimedia objects. However, the vast majority of these tools only analyze one modality at a time.<br> This was not a problem as long as still images were considered the most widely edited media, but now, since manipulated videos are becoming customary, performing monomodal analyses could be reductive. Nonetheless, there is a lack in the literature regarding multimodal detectors (systems that consider both audio and video components). This is due to the difficulty of developing them but also to the scarsity of datasets containing forged multimodal data to train and test the designed algorithms.</p> <p>In this paper we focus on the generation of an audio-visual deepfake dataset.<br> First, we present a general pipeline for synthesizing speech deepfake content from a given real or fake video, facilitating the creation of counterfeit multimodal material. The proposed method uses Text-to-Speech (TTS) and Dynamic Time Warping (DTW) techniques to achieve realistic speech tracks. Then, we use the pipeline to generate and release TIMIT-TTS, a synthetic speech dataset containing the most cutting-edge methods in the TTS field. This can be used as a standalone audio dataset, or combined with DeepfakeTIMIT and VidTIMIT video datasets to perform multimodal research. Finally, we present numerous experiments to benchmark the proposed dataset in both monomodal (i.e., audio) and multimodal (i.e., audio and video) conditions.<br> This highlights the need for multimodal forensic detectors and more multimodal deepfake data.</p> <ul> <li>For the initial version of TIMIT-TTS <strong>v1.0</strong> <ul> <li>Arxiv: https://arxiv.org/abs/2209.08000</li> <li>TIMIT-TTS Database v1.0: https://zenodo.org/record/6560159</li> </ul> </li> </ul>
CRPIH_UVigo-GL-Voices: Galician TTS dataset
<p><em>CRPIH_UVigo-GL-Voices</em> is a Galician TTS multi-speaker dataset containing audio recordings from four different speakers (two female and two male voices). The characteristics of each voice are detailed in the table below:</p> <table> <tbody> <tr> <td><strong>Voice Name</strong></td> <td><strong>Gender</strong></td> <td><strong>Speaker</strong></td> <td><strong>Recording</strong></td> <td><strong># Utts</strong></td> <td><strong>Duration</strong></td> <td><strong>Sampling Rate</strong></td> <td><strong>Format</strong></td> </tr> <tr> <td>Sabela</td> <td>Female</td> <td>Professional radio broadcaster</td> <td>Professional studio</td> <td>9,999</td> <td>14h 28m</td> <td>16kHz/ 44kHz</td> <td>16-bit PCM</td> </tr> <tr> <td>Icía</td> <td>Female</td> <td>Amateur</td> <td>Semi-professional studio</td> <td>2,950</td> <td>4h 5m</td> <td>16kHz/ 96kHz</td> <td>16/24-bit PCM</td> </tr> <tr> <td>Iago</td> <td>Male</td> <td>Amateur</td> <td>Radio studio</td> <td>1,316</td> <td>1h 13m</td> <td>16kHz/ 48kHz</td> <td>16-bit PCM</td> </tr> <tr> <td>Paulo</td> <td>Male</td> <td>Amateur</td> <td>Radio studio</td> <td>1,316</td> <td>1h 15m</td> <td>16kHz</td> <td>16-bit PCM</td> </tr> </tbody> </table> <p> </p> <p>Each speaker recorded a subset of utterances from a text corpus of 10,000 sentences, with a length between 1 and 44 words. This corpus is mainly composed of press excerpts, but it also contains a small subset of manually designed sentences. The press excerpts were extracted from newspapers published before 2010 ("O Correo Galego", "Galicia Hoxe" and "Vieiros"), whereas the hand-crafted sentences were created at the CRPIH in the year 1999.</p> <p>The data is organized into folders with each folder corresponding to one of the speakers. Each speaker's folder is composed of the following subdirectories:</p> <ul> <li><strong>txt</strong> → Audio transcripts enconded in ISO 8859-1.</li> <li><strong>fon</strong> → Phoneme-level forced alignment between phonemic transcriptions (provided by Cotovía) and audio recordings.</li> <li><strong>wav_[bit depth]bits_[sampling rate]kHz</strong> → WAV format audio files at [sampling-rate]kHz [bit-depth]-bit (see table above).</li> </ul> <p>The file naming convention is as follows: two lowercase elements indicating the creators of the dataset (“<em>crpih_uvigo</em>”), the ISO code for the Galician language (“<em>gl</em>”), the name of the voice (e. g., “<em>sabela</em>”), and a 5-digit number identifying the utterance. All components are separated by underscores (e. g., “<em>crpih_uvigo_gl_sabela_00001.txt</em>”). For some of the speakers, there is an additional element for identifying dataset's splits (e. g., “<em>crpih_uvigo_gl_iago_a_00001.txt</em>” and “<em>crpih_uvigo_gl_iago_b_00001.txt</em>”).</p> <p><strong>Acknowledgements</strong></p> <p>We would like to thank the speakers for recording and donating their voices.</p>
Dominant Rural Technological Trajectories (TTs) dataset at municipality level of the Brazilian Legal Amazon (BLA)
<p>This dataset contains the dominant technological trajectories (TTs) of the Brazilian Legal Amazon (BLA) municipalities for the years of 1995, 2006 and 2017. The dominant trajectory is the one, among the six identified by Costa (2021), that is economically most important in the municipality. The relative share of the Gross Value of Rural Production of the trajectory in the total Gross Value of Rural Production in the municipality was taken as a proxy of economic importance. From the tabulation of the datasets in Costa (2022), the dominant technological trajectory was identified, it means, considering the methodology used, which of the six TTs was responsible for over 50% of the municipal Gross Value of Rural Production. The dominant TTs were calculated using the official municipal grid for the year the agricultural census was carried out.</p> <p>The dataset is organized as a .csv table, for each year (1995, 2006 and 2017), with a geographic key for each municipality (6-digit municipality code, 2-digit state code), that can be easily linked with municipality available shapefiles and other datasets. </p> <p>References:</p> <p>Costa, F. A. Structural diversity and change in rural Amazonia: a comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). <strong>Nova econ</strong>. 31 (02), May-Aug 2021, doi:10.1590/0103-6351/6373.</p> <p>Costa, F. A, (2022). Database of Rural Technological Trajectories of the Legal Amazon delimited by the Method of Differentiation and Structural Signification of Rural Production (M-DASTRU). <strong>Zenodo.</strong> DOI: 10.5281/zenodo.7035753</p>
Dominant Rural Technological Trajectories (TTs) dataset at municipality level of the Amazon Biome
<p>This dataset contains the dominant technological trajectories (TTs) of the Amazon Biome municipalities for the years of 1995, 2006 and 2017. The dominant trajectory is the one, among the six identified by Costa (2021), that is economically most important in the municipality. The relative share of the Gross Value of Rural Production of the trajectory in the total Gross Value of Rural Production in the municipality was taken as a proxy of economic importance. From the tabulation of the datasets in Costa (2021), the dominant technological trajectory was identified, it means, considering the methodology used, which of the six TTs was responsible for over 50% of the municipal Gross Value of Rural Production. The dominant TTs were calculated using the official municipal grid for the year the agricultural census was carried out.</p> <p>The dataset is organized as a .csv table, for each year (1995, 2006 and 2017), with a geographic key for each municipality (6-digit municipality code, 2-digit state code), that can be easily linked with municipality available shapefiles and other datasets. </p> <p>References:</p> <p>Costa, F. A. Structural diversity and change in rural Amazonia: a comparative assessment of the technological trajectories based on agricultural censuses (1995, 2006 and 2017). <strong>Nova econ. </strong>31 (02), May-Aug 2021, doi:10.1590/0103-6351/6373.</p>
Traumatologic Acute Pain Management With Fentanyl Transdermal Therapeutic System (TTS)
ClinicalTrials.gov study NCT04026022. IPD Sharing: YES. Countries: 1. Publications: 1.
TUNDRA - A Multilingual Corpus of Found Data for TTS Research Created with Light Supervision,
<p>The corpus is described in:</p> <p><strong>A. Stan, O. Watts, Y. Mamiya, M. Giurgiu, R. A. J. Clark, J. Yamagishi, S. King,</strong> <em>TUNDRA: A Multilingual Corpus of Found Data for TTS Research Created with Light Supervision</em>, In Proc. Interspeech, Lyon, France, August 2013</p> <p> </p> <p> </p> <pre>############################################################### ## ## ## THE SIMPLE4ALL TUNDRA CORPUS ## ## version 1.0 ## ## ## ############################################################### Simple4All Tundra (version 1.0) is the first release of a standardised multilingual corpus designed for text-to-speech research with imperfect or found data. The corpus consists of approximately 60 hours of speech data from audiobooks in 14 languages, as well as utterance-level alignments obtained with a lightly-supervised process. Most audiobooks are from the public domain and allow redistribution. However, some have restricted use, and in those cases the segmented and aligned data cannot be downloaded from our website. --------------------------------------------------------------- LICENCE --------------------------------------------------------------- This work is licensed under a Creative Commons Attribution 3.0 Unported License http://creativecommons.org/licenses/by/3.0/ This licence applies to the selection, segmentation and alignment of the speech and text data. The underlying audio and text are licensed under their specific datasource terms. Please refer to the links below for a full description of them. If you use any part of the corpus in your work, please cite the following paper: A. Stan, O. Watts, Y. Mamiya, M. Giurgiu, R. A. J. Clark, J. Yamagishi, S. King, TUNDRA: A Multilingual Corpus of Found Data for TTS Research Created with Light Supervision, In Proc. Interspeech, Lyon, France, August 2013 --------------------------------------------------------------- SPEECH AND TEXT SOURCES --------------------------------------------------------------- 1) Bulgarian - "Zhetvariat" by Yordan Yovkov audio: http://librivox.org/zhetvariat-by-yordan-yovkov text: http://slovo.bg/showwork.php3?AuID=95&WorkID=9610&Level=1 2)Danish - "Grimms eventyr I udvalg" by Grimm Brothers audio: http://librivox.org/grimms-eventyr-i-udvalg-by-br%C3%B8drene-grimm text: http://www.estrup.org/cms/?mod=text&id=392 3)Dutch- "Anna Karenina" by Leo Tolstoy audio: http://librivox.org/anna-karenina-by-leo-tolstoy text: http://www.gutenberg.org/ebooks/13214 4) English - "Living Alone" by Stella Benson audio: http://librivox.org/living-alone-by-stella-benson text: http://www.gutenberg.org/ebooks/14907 5) Finnish- "Rautatie" by Juhani Aho audio: http://librivox.org/rautatie-by-juhani-aho text: http://www.gutenberg.org/ebooks/10481 6) French - "Candide" by Voltaire audio: http://librivox.org/candide-by-voltaire text: http://www.gutenberg.org/cache/epub/4650/pg4650.txt 7) German - "Das Bildnis des Dorian Gray" by Oscar Wilde audio: http://librivox.org/das-bildnis-des-dorian-gray-by-oscar-wilde text: http://gutenberg.spiegel.de/buch/1836/1 8) Hungarian - "Egri csillagok" by Geza Gardonyi audio: http://gutenberg.spiegel.de/buch/1836/1 text: http://mek.oszk.hu/00600/00656/index.phtml 9) Italian - "Galatea" by Anton Giulio Barrili audio: http://librivox.org/galatea-by-anton-giulio-barrili/ text: http://www.gutenberg.org/ebooks/19427 10) Polish - "Siedem wybranyc opowiadan" by Wladyslaw Orkan audio: http://librivox.org/siedem-wybranych-opowiadan-by-wladyslaw-orkan/ text: http://pl.wikisource.org/wiki/Autor:W%C5%82adys%C5%82aw_Orkan 11) Portuguese - "Senhora" by Jose de Alencar audio: http://librivox.org/senhora-by-jose-de-alencar/ text: http://stat.correioweb.com.br/arquivos/educacao/arquivos/JosdeAlencar-Senhora0.pdf 12) Romanian - "Mara" by Ioan Slavici audio: http://speech.utcluj.ro/corpora/mara.html text: http://ro.wikisource.org/wiki/Mara 13) Russian - "Ucheniye Khrista" by Leo Tolstoy audio: http://librivox.org/teachings-of-christ-rus-by-leo-tolstoy/ text: http://az.lib.ru/t/tolstoj_lew_nikolaewich/text_0520.shtml 14) Spanish - "Don Quijote de la Mancha" by Miguel de Cervantes audio: http://www.quijote.es/IVCentenario_AudioLibro.php text: http://www.gutenberg.org/ebooks/5921 --------------------------------------------------------------- CONTENTS --------------------------------------------------------------- For each audiobook you can download the following information, with the exception of the Spanish audiobook which has a restricted use and the speech data cannot be downloaded from out website: 1) Segmented and aligned data -- http://tundra.simple4all.org/download.html -- an archive containing the results of the lightly supervised segmentation and alignment algorithm; -- folders and files (the following are the same for both training and test data sets): -- wav/ - speech data maintaining the original chapter names, but with additional indexes resulted from the sentence-level segmentation; -- txt/ - raw text files corresponding to each speech file from the wav/ folder; -- txtWithPunctuation/ - text files for each speech segment with punctuation restored from the original book text; -- speech_transcript.txt - a single file for all orthographic transcripts; -- a separate handmade test set data in the handmadeTest/ folder (see below for its description); 2) 1 hour subset of selected data -- http://tundra.simple4all.org/ssw8data.html -- an archive containing approximately 1 hour of selected audio used to train the voices from the Demo section; 3) Synthetic samples -- http://tundra.simple4all.org/ssw8data.html -- an archive containing the synthetic samples obtained with our lightly supervised TTS system for the handmade test set; 4) Chapter-level annotation -- http://tundra.simple4all.org/download.html -- a file with the chapter-level time alignment within the original data and the corresponding text for the confident data. --------------------------------------------------------------- SEGMENTATION AND ALIGNMENT --------------------------------------------------------------- Descriptions of the lightly supervised segmentation and alignment methods can be found in the following papers: 1) A. Stan, O. Watts, Y. Mamiya, M. Giurgiu, R. A. J. Clark, J. Yamagishi, S. King, TUNDRA: A Multilingual Corpus of Found Data for TTS Research Created with Light Supervision, In Proc. Interspeech, Lyon, France, August 2013 2) Adriana STAN, Peter BELL, Simon KING A grapheme-based method for automatic alignment of speech and text data, In Proc. IEEE Workshop on Spoken Language Technology, Miami, Florida, USA, December 2012 3) Yoshitaka Mamiya, Junichi Yamagishi, Oliver Watts, Robert A.J. Clark, Simon King and Adriana STAN Lightly Supervised GMM VAD to use Audiobook for Speech Synthesiser, In Proc. ICASSP, May 2013 The synthetic voice building algorithm is described in detail here: 4) O. Watts, A. Stan, R. Clark, Y. Mamiya, M. Giurgiu, J. Yamagishi, S. King, Unsupervised and lightly-supervised learning for rapid construction of TTS systems in multiple languages from ‘found’ data: evaluation and analysis, In Proc. SSW8, Barcelona, Spain, August 2013 With a similar approach being presented in: 5) O. Watts, A. Stan, A. Suni, M. Burgos, J.M. Montero, The Simple4All entry to the Blizzard Challenge 2013, Blizzard Challenge 2013 --------------------------------------------------------------- TRAIN/TEST DIVISION OF DATA --------------------------------------------------------------- Test material is taken from the ends of books, from enough whole chapters or stories to make up at least 10 min of audio of aligned data (NB more can be harvested from these chapters from the unaligned utterances). The exceptions are the Hungarian and Portuguese audiobooks in which the following chapters have variable recording conditions and are not considered suitable for comparisons: Hungarian: egricsillagok_[19-49] Portuguese: senhora_[14-20] and senhora_[23-41] The following chapters are reserved for testing: Bulgarian: zhetvariat_2{3,4,5}* Danish: eventyr_{08,09,10,11,12}* Dutch: annakarenina_021* German: doriangray_17* English: livingalone_{09,10}* Finnish: rautatie_{7,8}* French: candide_{29,30}* Hungarian: egricsillagok_{17,18}* Italian: galatea_{19,20}* Polish: siedemwybranchopowiadan_7* Portuguese: senhora_{12,13}* Romanian: mara_7{1,2}* Russian: teachingsofchrist_9* Spanish: Parte1_35* For the evaluations published in the following paper: O. Watts, A. Stan, R. Clark, Y. Mamiya, M. Giurgiu, J. Yamagishi, S. King, Unsupervised and lightly-supervised learning for rapid construction of TTS systems in multiple languages from 'found' data: evaluation and analysis, In Proc. SSW8, Barcelona, Spain, August 2013 a hand-segmented test set of about 40 utterances in all languages was prepared from the test chapters, so that various problems with the automatically aligned test utterances (sentence fragments, non-matching transcripts etc.) would not confuse the evaluation results. These hand segmented and aligned utterances are contained in the ./handmadeTest folder. Synthesised samples of this handmade test set are also available for download. --------------------------------------------------------------- CONTRIBUTORS --------------------------------------------------------------- Adriana Stan (Communications Department, Technical University of Cluj-Napoca) Oliver Watts (Centre for Speech Technology Research, University of Edinburgh) Yoshitaka Mamiya (Centre for Speech Technology Research, University of Edinburgh) Junichi Yamagishi (National Institute of Informatics, Tokyo) Mircea Giurgiu (Communications Department, Technical University of Cluj-Napoca) Rob Clark (Centre for Speech Technology Research, University of Edinburgh) Simon King (Centre for Speech Technology Research, University of Edinburgh) --------------------------------------------------------------- CONTACT --------------------------------------------------------------- Please send all you enquires regarding the Tundra Copus to one of the following e-mail addresses: adriana.stan@com.utcluj.ro owatts@inf.ed.ac.uk --------------------------------------------------------------- ACKNOWLEDGEMNTS --------------------------------------------------------------- The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement No 287678 (the Simple4All project - http://www.simple4all.org) The research presented here has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF: http://www.ecdf.ed.ac.uk). The ECDF is partially supported by the eDIKT initiative (http://www.edikt.org.uk). We would like to thank Mihai Nae from Cartea Sonora for releasing the Romanian data, as well as to all the volunteers at Librivox and Gutenberg for dedicating their time to distribute this wide variety of data. </pre>
TTS Esophageal HILZO Stent: A Safety and Feasibility Study
ClinicalTrials.gov study NCT03269903. IPD Sharing: NO. Countries: 1. Publications: 1.
Nitrate Use to Obtain Radial Spasm Embarrassment (NURSE - TTS Trial)
ClinicalTrials.gov study NCT04766151. IPD Sharing: NO. Countries: 1. Publications: 1.
Personalization of Visual Characters and Text-to-Speech (TTS) to Support Learning in Students With Reading Difficulties
ClinicalTrials.gov study NCT06947512. IPD Sharing: YES. Countries: 1. Publications: 0.
TTS Faculty Dossiers
Open the record for dataset details and reuse information.
Hummingbird TTS Ear Tube Delivery Study
ClinicalTrials.gov study NCT02165384. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Efficacy and Safety Study of Transdermal Therapeutic System (TTS) Fentanyl in Participants With Osteoarthritis Knee Pain
ClinicalTrials.gov study NCT01742897. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Study of the Hummingbird TTS™ Tympanostomy Tube System
ClinicalTrials.gov study NCT03503591. IPD Sharing: NO. Countries: 1. Publications: 0.
An Efficacy and Safety Study of Transdermal Therapeutic System (TTS)-Fentanyl in Participants With Osteoarthritis
ClinicalTrials.gov study NCT01774929. IPD Sharing: Not stated. Countries: 1. Publications: 0.
An Efficacy and Safety Study of Transdermal Therapeutic System (TTS)-Fentanyl in Severe Chronic Low Back Pain
ClinicalTrials.gov study NCT01774903. IPD Sharing: Not stated. Countries: 1. Publications: 0.
An Efficacy and Safety Study of Transdermal Therapeutic System (TTS)-Fentanyl in Thai Participants With Chronic Non-Malignant Pain
ClinicalTrials.gov study NCT01816243. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Nos_Celtia-GL: Galician TTS corpus
<p><em><strong>This corpus is publicly accessible upon accepting T&Cs and requesting access.</strong></em></p> <p><br>Galician TTS single speaker corpus of approximately 25 hours of speech.</p> <p>Nos_Celtia-GL is a phonetically and morphosyntactically rich corpus of 20,000 phrases (approximately 200,000 words) comprising two subcorpora: a previously compiled corpus created by the Grupo de Tecnoloxías Multimedia (GTM), together with the Centro Ramón Piñeiro para a Investigación en Humanidades (CRPIH), and a corpus compiled by the Nós Project from multi-domain texts.</p> <p>The text corpus statistics are detailed in the table below:</p> <table> <tbody> <tr> <td><strong>Subcorpus</strong></td> <td> <strong>Sentence no</strong>.</td> <td><strong>Word no. </strong></td> <td><strong>Sentence length (words)</strong></td> <td><strong>Sentence domain / type</strong></td> </tr> <tr> <td>GTM</td> <td>10,000</td> <td>121,726</td> <td>1-44</td> <td> <ul> <li>Journalistic (written) text</li> <li>Manually designed sentences (interrogative, exclamative, imperative, lists of numbers…)</li> </ul> </td> </tr> <tr> <td>Nós</td> <td>10,000</td> <td>99,622</td> <td> 1-36</td> <td> <ul> <li>21,8% transcripts of oral discourse</li> <li>17,5% dictionary definitions</li> <li>12.7% transcripts of parliamentary speeches</li> <li>20% transcripts of news broadcasts</li> <li>28% short (<4 words), interrogative, exclamative, imperative, and elliptical sentences</li> </ul> </td> </tr> </tbody> </table> <p> </p> <p>While the Nós subcorpus has undergone a thorough linguistic review, we have decided not to adapt the GTM corpus to the current grammatical norms of the Galician language with a view to obtaining a parallel corpus to the previously recorded <a href="../record/8027725">CRPIH_UVigo-GL-Voices</a>.</p> <p>Nos_Celtia-GL was recorded in a controlled environment (recording studio) by a professional female voice talent selected among four speakers through a perceptual listening test in which more than 50 participants assessed the speakers' clarity, prosody, likeability, and language proficiency.</p> <p>The file naming scheme of the audio files consists of a series of lowercase elements indicating the type of audio (raw), the creators of the corpus (nos), the name of the voice (celtia), and the ISO code for the Galician language (gl), followed by a 5-digit number identifying the utterance. All components are separated by underscores (e. g., raw_nos_celtia_gl_00001.wav).</p> <p>Metadata is provided in "metadata.csv". This file consists of one record per line, delimited by the vertical bar character (0x7c). The fields are:</p> <p> 1. Audio file: name of the corresponding .wav file</p> <p> 2. Transcription: non-normalized text read by speaker (UTF-8)</p> <p>The audio files are available in the format in which they were originally recorded, 48 kHz, 16-bit WAV format, and amount to approximately 25 hours.</p> <p>Version 1.0.0 contains the raw sound files with no editing nor normalization, together with the corresponding text.</p> <p>For more information, please go to <a href="https://nos.gal/">https://nos.gal/</a> or contact the Nós project at <a href="mailto:proxecto.nos@usc.gal">proxecto.nos@usc.gal</a>.</p> <p><strong>Terms and conditions</strong></p> <p>The property to the speech data contained in this dataset has been transferred to the University of Santiago de Compostela (USC) for the duration of 15 years. Starting 30/11/2037, this data will be removed. After this date, the USC is not liable for any use by third parties who might have downloaded the dataset. </p> <p><strong>Citing</strong></p> <p>Please refer to our paper for more details: <a title="https://www.isca-archive.org/iberspeech_2024/garciadiaz24_iberspeech.html" href="https://www.isca-archive.org/iberspeech_2024/garciadiaz24_iberspeech.html" target="_blank" rel="noreferrer noopener">Nos_Celtia-GL: an Open High-Quality Speech Synthesis Resource for Galician</a></p> <p>If you use this data in your work, please cite: García Díaz, N., Vázquez Abuín, M., Magariños, C., Vladu, A.I., Moscoso Sánchez, A., Fernández Rei, E. (2024) Nos_Celtia-GL: an Open High-Quality Speech Synthesis Resource for Galician. Proc. IberSPEECH 2024, 91-95, doi: 10.21437/IberSPEECH.2024-19</p> <p><strong>Funding and acknowledgements</strong></p> <p>"The Nós project: Galician in the society and economy of Artificial Intelligence" is possible thanks to the funding resulting from the agreement 2021-CP080 between the Xunta de Galicia and the University of Santiago de Compostela, and thanks to the Investigo program, within the National Recovery, Transformation and Resilience Plan, within the framework of the European Recovery Fund (NextGenerationEU).</p> <p>We would like to thank the speaker, Consuelo Díaz Isorna, for kindly providing her voice to this project.</p> <p>We would also like to thank the following entities for their kind collaboration in providing the data for the text corpus: <a href="http://gtm.uvigo.es/en/">Grupo de Tecnoloxías Multimedia (GTM)</a>, <a href="https://www.cirp.es/">Centro Ramón Piñeiro para a Investigación en Humanidades (CRPIH)</a>, <a href="https://academia.gal/inicio">Real Academia Galega</a>, <a href="https://www.crtvg.es/">Corporación Radio Televisión de Galicia S.A.</a>, <a href="https://www.parlamentodegalicia.gal/">Parlamento de Galicia</a>, and the <a href="http://ilg.usc.es/ago/">Arquivo do Galego Oral (ILG) project</a>.</p> <p>Our gratitude also to Xoán Carlos Goris García, Elia Lago Pereira and Alicia López Besteiro for reviewing part of the audio corpus.</p>
An Efficacy and Safety Study of Low-dose Transdermal Therapeutic System (TTS)-Fentanyl D-Trans in Taiwan Participants With Cancer Pain
ClinicalTrials.gov study NCT00771199. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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