Interpreting Technologies : Introduction

Introducció a la secció monogràfica Dossier Tradumàtica sobre tecnologíes de la interpretació.Introduction to the monographic section Dossier Tradumàtica on Interpreting Technologies.Introducción de la sección monográfica Dosier Tradumática sobre tecnologías de la interpretación

A survey on Automatic Speech Recognition systems for Portuguese language and its variations

Communication has been an essential part of being human and living in society. There are several different languages and variations of them, so you can speak English in one place and not be able to communicate effectively with someone who speaks English with a different accent. There are several application areas where voice/speech data can be of importance, such as health, security, biometric analysis or education. However, most studies focus on English, Arabic or Asian languages, neglecting other relevant languages, such as Portuguese, which leaves their investigations wide open. Thus, it is crucial to understand the area, where the main focus is: what are the most used techniques for feature extraction and classification, and so on. This paper presents a survey on automatic speech recognition components for Portuguese-based language and its variations, as an understudied language. With a total of 101 papers from 2012 to 2018, the Portuguese-based automatic speech recognition field tendency will be explained, and several possible unexplored methods will be presented and discussed in a collaborative and overall way as our main contribution

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT

Getting Past the Language Gap: Innovations in Machine Translation

In this chapter, we will be reviewing state of the art machine translation systems, and will discuss innovative methods for machine translation, highlighting the most promising techniques and applications. Machine translation (MT) has benefited from a revitalization in the last 10 years or so, after a period of relatively slow activity. In 2005 the field received a jumpstart when a powerful complete experimental package for building MT systems from scratch became freely available as a result of the unified efforts of the MOSES international consortium. Around the same time, hierarchical methods had been introduced by Chinese researchers, which allowed the introduction and use of syntactic information in translation modeling. Furthermore, the advances in the related field of computational linguistics, making off-the-shelf taggers and parsers readily available, helped give MT an additional boost. Yet there is still more progress to be made. For example, MT will be enhanced greatly when both syntax and semantics are on board: this still presents a major challenge though many advanced research groups are currently pursuing ways to meet this challenge head-on. The next generation of MT will consist of a collection of hybrid systems. It also augurs well for the mobile environment, as we look forward to more advanced and improved technologies that enable the working of Speech-To-Speech machine translation on hand-held devices, i.e. speech recognition and speech synthesis. We review all of these developments and point out in the final section some of the most promising research avenues for the future of MT