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4 results for “automatic translation”
Data Cleaning, Translation & Split of the Dataset for the Automatic Classification of Documents for the Classification System for the Berliner Handreichungen zur Bibliotheks- und Informationswissenschaft
<ul> <li>Cleaned_Dataset.csv – The combined CSV files of all scraped documents from DABI, e-LiS, o-bib and Springer.</li> <li>Data_Cleaning.ipynb – The Jupyter Notebook with python code for the analysis and cleaning of the original dataset.</li> <li>ger_train.csv – The German training set as CSV file.</li> <li>ger_validation.csv – The German validation set as CSV file.</li> <li>en_test.csv – The English test set as CSV file.</li> <li>en_train.csv – The English training set as CSV file.</li> <li>en_validation.csv – The English validation set as CSV file.</li> <li>splitting.py – The python code for splitting a dataset into train, test and validation set.</li> <li>DataSetTrans_de.csv – The final German dataset as a CSV file.</li> <li>DataSetTrans_en.csv – The final English dataset as a CSV file.</li> <li>translation.py – The python code for translating the cleaned dataset.</li> </ul>
Figure 1. The Structure of the Automatic Translate Voice to Sign Language Animation System-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>All technologies of voice recognition, speaker identification and verification, each has its<br> own advantages and disadvantages and may requires different treatments and techniques. The<br> choice of which technology to use is application-specific. At the highest level, all voice recognition<br> systems contain two main modules: feature extraction and feature matching. Feature extraction is<br> the process that extracts a small amount of data from the voice signal that can later be used to<br> represent each word. Feature matching involves the actual procedure to identify the unknown word<br> by comparing extracted features from his/her voice input with the ones from a set of known words.<br> A wide range of possibilities exist for parametrically representing the speech signal for the<br> voice recognition task, such as Linear Prediction Coding (LPC), RASTA-PLP and Mel-Frequency<br> Cepstrum Coefficients (MFCC).</p>
Automatic translation and multilingual cultural heritage retrieval: a case study with transcriptions in Europeana (dataset)
<p>The dataset contains all the data required to reproduce the experiments done in the paper "Automatic translation and multilingual cultural heritage retrieval: a case study with transcriptions in Europeana", published in the 25th International Conference on Theory and Practice of Digital Libraries (<a href="http://www.tpdl.eu/tpdl2021/">TPDL'21</a>). In that work we run an experiment using the Europeana CH digital library as a use case, and we evaluated the effectiveness of a multilingual information retrieval strategy using machine translations to English as pivot language. We used the CEF translation service (eTranslation) for the translation of queries and content to English (<a href="https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL/eTranslation">https://ec.europa.eu/cefdigital/wiki/display/CEFDIGITAL/eTranslation</a>).</p> <p>The dataset is also available at <a href="https://rnd-2.eanadev.org/share/crosslingual-search/">https://rnd-2.eanadev.org/share/crosslingual-search/</a>, and it is organized in four main folders:</p> <ul> <li><strong>queries</strong>: sample of 68 queries and their translations to English. The queries were issued in languages other than English from the Europeana Portal, using the Europeana’s 1914-1918 thematic collection, between January and August 2019.</li> <li><strong>transcriptions</strong>: sample of 18,257 handwriting transcriptions and its translations to English. The transcriptions are taken from the Europeana 1914-1918 thematic collection, and obtained from the Transcribathon crowdsourcing platform (https://europeana.transcribathon.eu/).</li> <li><strong>solr_configuration</strong>: Apache Solr search engine configuration used in the experiments (which replicates the one used in Europeana).</li> <li><strong>results</strong>: manual evaluation of the query translations, and automatic evaluation of the multilingual retrieval.</li> </ul> <p> </p>
Eyetracking and keystroke logging metrics for measuring automaticity in translation
<p>This dataset contains the behavioural data from 35 novice translators and 30 experienced translators (65 participants in total) during their translation process. The data are specifically generated eyetracking and keystroke logging metrics for measuring automaticity in the complex activity of translation, which includes four aspects, namely, speed, degree of parallel processing, effortlessness, and the pattern of attention allocated to the source text and target text areas. The dataset is provided for the paper Automaticity in Translation: Effects of Time Pressure and Work Experience.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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)
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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.