47,788 research outputs found
Computerization of African languages-French dictionaries
This paper relates work done during the DiLAF project. It consists in
converting 5 bilingual African language-French dictionaries originally in Word
format into XML following the LMF model. The languages processed are Bambara,
Hausa, Kanuri, Tamajaq and Songhai-zarma, still considered as under-resourced
languages concerning Natural Language Processing tools. Once converted, the
dictionaries are available online on the Jibiki platform for lookup and
modification. The DiLAF project is first presented. A description of each
dictionary follows. Then, the conversion methodology from .doc format to XML
files is presented. A specific point on the usage of Unicode follows. Then,
each step of the conversion into XML and LMF is detailed. The last part
presents the Jibiki lexical resources management platform used for the project.Comment: 8 page
Basque and Spanish Multilingual TTS Model for Speech-to-Speech Translation
[EN] Lately, multiple Text-to-Speech models have emerged using Deep Neural networks to
synthesize audio from text. In this work, the state-of-the-art multilingual and
multi-speaker Text-to-Speech model has been trained in Basque, Spanish, Catalan, and
Galician. The research consisted of gathering the datasets, pre-processing their audio and
text data, training the model in the languages in different steps, and evaluating the
results at each point. For the training step, a transfer learning approach has been used
from a model already trained in three languages: English, Portuguese, and French.
Therefore, the final model created here supports a total of seven languages. Moreover,
these models also support zero-shot voice conversion, using an input audio file as a
reference. Finally, a prototype application has been created to do Speech-to-Speech
Translation, putting together the models trained here and other models from the
community. Along the way, some Deep Speech Speech-to-Text models have been
generated for Basque and Galician.[EU] Azkenaldian, Text-to-Speech eredu anitz sortu dira sare neuronal sakonak erabiliz, testutik audioa sintetizatzeko. Lan honetan, state-of-the-art Text-to-Speech eredu
eleaniztun eta hiztun anitzeko eredua landu da euskaraz, gaztelaniaz, katalanez eta
galegoz. Ikerketa honetan datu-multzoak bildu, haien audio- eta testu-datuak aldez
aurretik prozesatu, eredua hizkuntzetan entrenatu da urrats desberdinetan eta emaitzak
puntu bakoitzean ebaluatu dira. Entrenatze-urratserako, ikaskuntza-transferentzia
teknika erabili da dagoeneko hiru hizkuntzatan trebatutako eredu batetik abiatuta:
ingelesa, portugesa eta frantsesa. Beraz, hemen sortutako azken ereduak zazpi hizkuntza
onartzen ditu guztira. Gainera, eredu hauek zero-shot ahots bihurketa ere egiten dute,
sarrerako audio fitxategi bat erreferentzia gisa erabiliz. Azkenik, Speech-to-Speech
Translation egiteko prototipo aplikazio bat sortu da hemen entrenatutako ereduak eta
komunitateko beste eredu batzuk elkartuz. Bide horretan, Deep Speech Speech-to-Text
eredu batzuk sortu dira euskararako eta galegorako
A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation
Interlingua based Machine Translation (MT) aims to encode multiple languages
into a common linguistic representation and then decode sentences in multiple
target languages from this representation. In this work we explore this idea in
the context of neural encoder decoder architectures, albeit on a smaller scale
and without MT as the end goal. Specifically, we consider the case of three
languages or modalities X, Z and Y wherein we are interested in generating
sequences in Y starting from information available in X. However, there is no
parallel training data available between X and Y but, training data is
available between X & Z and Z & Y (as is often the case in many real world
applications). Z thus acts as a pivot/bridge. An obvious solution, which is
perhaps less elegant but works very well in practice is to train a two stage
model which first converts from X to Z and then from Z to Y. Instead we explore
an interlingua inspired solution which jointly learns to do the following (i)
encode X and Z to a common representation and (ii) decode Y from this common
representation. We evaluate our model on two tasks: (i) bridge transliteration
and (ii) bridge captioning. We report promising results in both these
applications and believe that this is a right step towards truly interlingua
inspired encoder decoder architectures.Comment: 10 page
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