scholarly journals Language Technology Platform for Public Administration

2020 ◽  
Author(s):  
Raivis Skadiņš ◽  
Mārcis Pinnis ◽  
Artūrs Vasiļevskis ◽  
Andrejs Vasiļjevs ◽  
Valters Šics ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Data Storage ◽  
Speech Synthesis ◽  
Computer Assisted ◽  
Translation Memory ◽  
Technology Platform ◽  
Future Developments ◽  
Language Technology ◽  
Language Data ◽  
Main Components

The paper describes the Latvian e-government language technology platform HUGO.LV. It provides an instant translation of text snippets, formatting-rich documents and websites, an online computer-assisted translation tool with a built-in translation memory, a website translation widget, speech recognition and speech synthesis services, a terminology management and publishing portal, language data storage, analytics, and data sharing functionality. The paper describes the motivation for the creation of the platform, its main components, architecture, usage statistics, conclusions, and future developments. Evaluation results of language technology tools integrated in the platform are provided.

scholarly journals Speech Synthesis in the Translation Revision Process: Evidence from Error Analysis, Questionnaire, and Eye-Tracking

Informatics ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 51
Author(s):  
Dragoş Ciobanu ◽  
Valentina Ragni ◽  
Alina Secară
Keyword(s):  
Speech Recognition ◽  
Eye Tracking ◽  
Speech Synthesis ◽  
Computer Assisted ◽  
Revision Process ◽  
External Resources ◽  
Technological Developments ◽  
Training And Research ◽  
Relevant Topic

Translation revision is a relevant topic for translator training and research. Recent technological developments justify increased focus on embedding speech technologies—speech synthesis (text-to-speech) and speech recognition (speech-to-text)—into revision workflows. Despite some integration of speech recognition into computer-assisted translation (CAT)/translation environment tools (TEnT)/Revision tools, to date we are unaware of any CAT/TEnT/Revision tool that includes speech synthesis. This paper addresses this issue by presenting initial results of a case study with 11 participants exploring if and how the presence of sound, specifically in the source text (ST), affects revisers’ revision quality, preference and viewing behaviour. Our findings suggest an improvement in revision quality, especially regarding Accuracy errors, when sound was present. The majority of participants preferred listening to the ST while revising, but their self-reported gains on concentration and productivity were not conclusive. For viewing behaviour, a subset of eye-tracking data shows that participants focused more on the target text (TT) than the source regardless of the revising condition, though with differences in fixation counts, dwell time and mean fixation duration (MDF). Orientation and finalisation phases were also identified. Finally, speech synthesis appears to increase perceived alertness, and may prompt revisers to consult external resources more frequently.

A Design of a Speech Conversion System from PuTongHua to Cantonese Based on iFLY MSP 2.0

2013 ◽  
Vol 373-375 ◽  
pp. 504-508
Author(s):  
Rong Gui Ma ◽  
Fang Zhou Liu
Keyword(s):  
Speech Recognition ◽  
Learning System ◽  
Computer Assisted ◽  
Text To Speech ◽  
Computer Assisted Learning ◽  
Key Technology ◽  
Conversion System ◽  
Recognition Function ◽  
Text Information ◽  
The Voice

The paper analyzes the working theory of a Speech Conversion System from PuTongHua to Cantonese based on iFLY MSP 2.0. In the system, QISR interface is chosen to complete speech recognition function which is the key technology to convert the voice information into the corresponding text information. Moreover, the QTTS interface is chosen to complete the text to speech function which is the key technology to transform the text which is the result of the speech recognition into the spoken information in Cantonese and then output. Finally, the computer assisted learning system is designed successfully in the environment of Visual C++ 6.0.

scholarly journals Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages

2021 ◽  
Author(s):  
Tanmai Khanna ◽  
Jonathan N. Washington ◽  
Francis M. Tyers ◽  
Sevilay Bayatlı ◽  
Daniel G. Swanson ◽  
...  
Keyword(s):  
Open Source ◽  
Machine Translation ◽  
Lexical Selection ◽  
Rule Based ◽  
Low Resource ◽  
Language Technology ◽  
Language Data ◽  
Recursive Structures ◽  
Platform Translation ◽  
Free Open Source

AbstractThis paper presents an overview of Apertium, a free and open-source rule-based machine translation platform. Translation in Apertium happens through a pipeline of modular tools, and the platform continues to be improved as more language pairs are added. Several advances have been implemented since the last publication, including some new optional modules: a module that allows rules to process recursive structures at the structural transfer stage, a module that deals with contiguous and discontiguous multi-word expressions, and a module that resolves anaphora to aid translation. Also highlighted is the hybridisation of Apertium through statistical modules that augment the pipeline, and statistical methods that augment existing modules. This includes morphological disambiguation, weighted structural transfer, and lexical selection modules that learn from limited data. The paper also discusses how a platform like Apertium can be a critical part of access to language technology for so-called low-resource languages, which might be ignored or deemed unapproachable by popular corpus-based translation technologies. Finally, the paper presents some of the released and unreleased language pairs, concluding with a brief look at some supplementary Apertium tools that prove valuable to users as well as language developers. All Apertium-related code, including language data, is free/open-source and available at https://github.com/apertium.

scholarly journals La tecnología habla-texto como herramienta de documentación para intérpretes: Nuevo método para compilar un corpus ad hoc y extraer terminología a partir de discursos orales en vídeo

2020 ◽  
pp. 263-281
Author(s):  
Mahmoud Gaber ◽  
Gloria Corpas Pastor ◽  
Ahmed Omer
Keyword(s):  
Climate Change ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Domain Knowledge ◽  
Ad Hoc ◽  
Central Component ◽  
Computer Assisted ◽  
Documentation Tool

Although interpreting has not yet benefited from technology as much as its sister field, translation, interest in developing tailor-made solutions for interpreters has risen sharply in recent years. In particular, Automatic Speech Recognition (ASR) is being used as a central component of Computer-Assisted Interpreting (CAI) tools, either bundled or standalone. This study pursues three main aims: (i) to establish the most suitable ASR application for building ad hoc corpora by comparing several ASR tools and assessing their performance; (ii) to use ASR in order to extract terminology from the transcriptions obtained from video-recorded speeches, in this case talks on climate change and adaptation; and (iii) to promote the adoption of ASR as a new documentation tool among interpreters. To the best of our knowledge, this is one of the first studies to explore the possibility of Speech-to-Text (S2T) technology for meeting the preparatory needs of interpreters as regards terminology and background/domain knowledge.

Psychometric Functions for Shortened Administrations of a Speech Recognition Approach Using Tri-Word Presentations and Phonemic Scoring

2012 ◽  
Vol 55 (3) ◽  
pp. 879-891 ◽  
Author(s):  
Stanley A. Gelfand ◽  
Jessica T. Gelfand
Keyword(s):  
Speech Recognition ◽  
Young Adult ◽  
Word Recognition ◽  
Recognition Test ◽  
Normal Hearing ◽  
Original Study ◽  
Computer Assisted ◽  
Signal To Noise ◽  
Adult Participants

Method Complete psychometric functions for phoneme and word recognition scores at 8 signal-to-noise ratios from −15 dB to 20 dB were generated for the first 10, 20, and 25, as well as all 50, three-word presentations of the Tri-Word or Computer Assisted Speech Recognition Assessment (CASRA) Test (Gelfand, 1998) based on the results of 12 normal-hearing young adult participants from the original study. Results The psychometric functions for both phoneme and word scores were very similar and essentially overlapping for all set sizes. Performance on the shortened tests accounted for 98.8% to 99.5% of the full (50-set) test variance with phoneme scoring, and 95.8% to 99.2% of the full test variance with word scoring. Shortening the tests accounted for little if any of the variance in the slopes of the functions. Conclusions The psychometric functions for abbreviated versions of the Tri-Word speech recognition test using 10, 20, and 25 presentation sets were described and are comparable to those of the original 50-presentation approach for both phoneme and word scoring in healthy, normal-hearing, young adult participants.

Sign in / Sign up

Export Citation Format

Share Document