Roldano Cattoni

A Multi-Perspective Evaluation of the NESPOLE! Speech-to-Speech Translation System

Performance and usability of real-world speech-to-speech translation systems, like the one developed within the NESPOLE! project, are affected by several aspects that go beyond the pure translation quality provided by the underlying components of the system. In this paper we describe these aspects as perspectives along which we have evaluated the NESPOLE! system. Four main issues are investigated: (1) assessing system performance under various network traffic conditions; (2) a study on the usage and utility of multi-modality in the context of multi-lingual communication; (3) a comparison of the features of the individual speech recognition engines, and (4) an end-to-end evaluation of the system.

Balancing Expressiveness and Simplicity in an Interlingua for Task Based Dialogue

In this paper we compare two interlingua representations for speech translation. The basis of this paper is a distributional analysis of the C-STAR II and NESPOLE databases tagged with interlingua representations. The C-STAR II database has been partially re-tagged with the NESPOLE interlingua, which enables us to make comparisons on the same data with two types of interlinguas and on two types of data (C-STAR II and NESPOLE) with the same interlingua. The distributional information presented in this paper show that the NESPOLE interlingua maintains the language-independence and simplicity of the C-STAR II speech-act-based approach, while increasing semantic expressiveness and scalability.

Robust analysis of spoken input combining statistical and knowledge-based information sources

The paper is concerned with the analysis of automatic transcription of spoken input into an interlingua formalism for a speech-to-speech machine translation system. This process is based on two sub-tasks: (1) the recognition of the domain action (a speech act and a sequence of concepts); (2) the extraction of arguments consisting of feature-value information. Statistical models are used for the former, while a knowledge-based approach is employed for the latter. The paper proposes an algorithm that improves the analysis in terms of robustness and performance; it combines the scores of the statistical models with the extracted arguments, taking into account the well-formedness constraints defined by the interlingua formalism.

A web-based demonstrator of a multi-lingual phrase-based translation system

This paper describes a multi-lingual phrase-based Statistical Machine Translation system accessible by means of a Web page. The user can issue translation requests from Arabic, Chinese or Spanish into English. The same phrase-based statistical technology is employed to realize the three supported language-pairs. New language-pairs can be easily added to the demonstrator. The Web-based interface allows the use of the translation system to any computer connected to the Internet.