MINTZAI: End-to-end Deep Learning for Speech Translation

被引：0

作者：

Etchegoyhen, Thierry ^{[1
]}

Arzelus, Haritz ^{[1
]}

Gete, Harritxu ^{[1
]}

Alvarez, Aitor ^{[1
]}

Hernaez, Inma ^{[2
]}

Navas, Eva ^{[2
]}

Gonzalez-Docasal, Ander ^{[1
]}

Osacar, Jaime ^{[1
]}

Benites, Edson ^{[1
]}

Ellakuria, Igor ^{[3
]}

Calonge, Eusebi ^{[4
]}

Martin, Maite ^{[4
]}

机构：

[1] Basque Res & Technol Alliance BRTA, Vicomtech Fdn, Mendaro, Gipuzkoa, Spain

[2] Univ Basque Country, HiTZ Ctr Aholab, UPV EHU, Leioa, Spain

[3] ISEA, Arrasate Mondragon, Gipuzkoa, Spain

[4] Ametzagaina, Donostia San Sebastian, Gipuzkoa, Spain

来源：

PROCESAMIENTO DEL LENGUAJE NATURAL | 2020年 / 65期

关键词：

Speech Translation; Machine Translation; Speech Recognition; Text to Speech; Deep Learning;

D O I：

10.26342/2020-65-12

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Speech Translation consists in translating speech in one language into text or speech in a different language. These systems have numerous applications, particularly in multilingual communities such as the European Union. The standard approach in the field involves the chaining of separate components for speech recognition, machine translation and speech synthesis. With the advances made possible by artificial neural networks and Deep Learning, training end-to-end speech translation systems has given rise to intense research and development activities in recent times. In this paper, we review the state of the art and describe project mintzai, which is being carried out in this field.

引用

页码：97 / 100

页数：4

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