LC-STAR II is a follow-up project of the EU funded project LC-STAR (Lexica and Corpora for Speech-to-Speech Translation Components, IST-2001-32216). LC-STAR II develops large lexica containing information for speech processing in ten languages targeting especially automatic speech recognition and text to speech synthesis but also other applications like speech-to-speech translation and tagging. The project follows by large the specifications developed within the scope of LC-STAR covering thirteen languages: Catalan, Finnish, German, Greek, Hebrew, Italian, Mandarin Chinese, Russian, Turkish, Slovenian, Spanish, Standard Arabic and US-English. The ten new LC-STAR II languages are: Brazilian-Portuguese, Cantonese, Czech, English-UK, French, Hindi, Polish, Portuguese, Slovak, and Urdu. The project started in 2006 with a lifetime of two years. The project is funded by a consortium, which includes Microsoft (USA), Nokia (Finland), NSC (Israel), Siemens (Germany) and Harmann/Becker (Germany). The project is coordinated by UPC (Spain) and validation is performed by SPEX (The Netherlands), and CST (Denmark). The developed language resources will be shared among partners.This paper presents a summary of the creation of word lists and lexica and an overview of adaptations of the specifications and conceptual representation model from LC-STAR to the new languages. The validation procedure will be presented too.

In the framework of the EU funded project TC-STAR (Technology and Corpora for Speech to Speech Translation),research on TTS aims on providing a synthesized voice sounding like the source speaker speaking the target language. To progress in this direction, research is focused on naturalness, intelligibility, expressivity and voice conversion both, in the TC-STAR framework. For this purpose, specifications on large, high quality TTS databases have been developed and the data have been recorded for UK English, Spanish and Mandarin. The development of speech technology in TC-STAR is evaluation driven. Assessment of speech synthesis is needed to determine how well a system or technique performs in comparison to previous versions as well as other approaches (systems & methods). Apart from testing the whole system, all components of the system will be evaluated separately. This approach grants better assesment of each component as well as identification of the best techniques in the different speech synthesisprocesses.This paper describes the specifications of Language Resources for speech synthesis and the specifications for evaluation of speech synthesis activities.

Especially in the area of early and preventive intervention, attachment theory has proved its practical relevance. The applicability of attachment theory and research has been shown with respect to principles and research findings, as well as intervention programmes enhancing young children’s socio-emotional development. However, the process of translating knowledge on attachment into practice requires systematic strategies to ensure programmes are effective in service delivery. Attachment theory and research have informed the development of early intervention programmes.

This paper presents specifications and requirements for creation and validation of large lexica that are needed in automatic Speech Recognition (ASR), Text-to-Speech (TTS) and statistical Speech-to-Speech Translation (SST) systems. The prepared language resources are created and validated within the scope of the EU-project LC-STAR (Lexica and Corpora for Speech-to-Speech Translation Components) during years 2002-2005. Large lexica consisting of phonetic, suprasegmental and morpho-syntactic content will be provided with well-documented specifications for 13 languages. A short summary of the LC-STAR project itself is presented. Overview about the specification for the corpora collection and word extraction as well as the specification and format of the lexica are presented. Particular attention is paid to the validation of the produced lexica and the lessons learnt during pre-validation. The created and validated language resources will be available via ELRA/ELDA.

This paper presents the corpora collection and lexica creation for the purposes of Automatic Speech Recognition (ASR) and Text-to-speech (TTS) that are needed in speech-to-speech translation (SST). These lexica will be specified, built and validated within the scope of the EU-project LC-STAR (Lexica and Corpora for Speech-to-Speech Translation Components) during the years 2002-2005. Large lexica consisting of phonetic, prosodic and morpho-syntactic content will be provided with well-documented specifications for at least 12 languages [1]. This paper provides a short overview of the speech-to-speech translation lexica in general as well as a summary of the LC-STAR project itself. More detailed information about the specification for the corpora collection and word extraction as well as the specification and format of the lexica are presented in later chapters.

The objective of the EU-project LC-STAR (Lexica and Corpora for Speech-to-Speech Translation Components) is corpora collection and lexica creation for the purposes of Automatic Speech Recognition (ASR) and Text-to-speech (TTS) that are needed in speech-to-speech translation (SST). During the lifetime of the project (2002-2005) these lexica will be specified, built and validated. Large lexica consisting of phonetic, prosodic and morpho-syntactic content will be provided with welldocumented specifications for at least 13 languages [1]. This project description provides a short overview of the speech-to-speech translation lexica in general as well as a summary of the LC-STAR project itself

Tying of Hidden Markov Model states is an important issue for the use of triphones as modeling units in automatic speech recognition systems. This paper studies the application of a–priori rules for tying in combination with data driven methods. The baseline method features a combination of a–priori rules that reduce the theoretical number of units by an oder of magnitude and a simple back–off tying. Back–off tying is based on the frequency of units appearing in the training material. The use of the a–priori rules has practical advantages especially for the implementation of continuous phoneme recognition. This method is compared to the widely used decision tree based clustering that makes no use of a–priori rules. A third method is proposed that combines a–priori rules with decision tree based clustering. Experiments on telephone data show that the combined method outperforms both other methods preserving the advantages of apply-ing a–priori rules.