The porting of a speech recognition system to a new language is usually a time-consuming and expensive process since it requires collecting, transcribing, and processing a large amount of language-specific training sentences. This work presents techniques for improved cross-language transfer of speech recognition systems to new target languages. Such techniques are particularly useful for target languages where minimal amounts of training data are available. We describe a novel method to produce a language-independent system by combining acoustic models from a number of source languages. This intermediate language-independent acoustic model is used to bootstrap a target-language system by applying language adaptation. For our experiments, we use acoustic models of seven source languages to develop a target Greek acoustic model. We show that our technique significantly outperforms a system trained from scratch when less than 8 h of read speech is available

We present a series of experiments designed to compare grammar-based and robust approaches to speech understanding, performed in the context of an Open Source medical speech translation system. We used two versions of the system, one grammar-based and one robust, trained off the same training data, and evaluated them on test data collected using both versions of the system. The experiments were constructed so as to avoid several methodological problems which occurred in earlier work reported in the literature. We found that the grammarbased version gave significantly better results than the robust version, with the difference increasing as subjects became more familiar with the system's coverage. The rate of improvement in subject performance was positively affected by providing them with an intelligent online help system.

In this paper, we present evidence that providing users of a speech to speech translation system for emergency diagnosis (MedSLT) with a tool that helps them to learn the coverage greatly improves their success in using the system. In MedSLT, the system uses a grammar-based recogniser that provides more predictable results to the translation component. The help module aims at addressing the lack of robustness inherent in this type of approach. It takes as input the result of a robust statistical recogniser that performs better for out-of-coverage data and produces a list of in-coverage example sentences. These examples are selected from a defined list using a heuristic that prioritises sentences maximising the number of N-grams shared with those extracted from the recognition result.

This work presents techniques for improved cross-language transfer of speech recognition systems to new, previously undeveloped, languages. Such techniques are particularly useful for target languages where minimal amounts of training data are available. We describe a novel method to produce a language-independent system by combining acoustic models from a number of source languages. This intermediate language-independent acoustic model is used to bootstrap a target-language system by applying language adaptation. For our experiments we use acoustic models of seven source languages to develop a target Greek acoustic model. We show that our technique significantly outperforms a system trained from scratch when less than 8 hours of read speech is available.

In this work, we present the creation of the first Greek Speech Corpus and the implementation of a Dictation System for workflow improvement in the field of journalism. The current work was implemented under the project called Logotypografia (Logos = logos, speech and Typografia = typography) sponsored by the General Secretariat of Research and Development of Greece. This paper presents the process of data collection (texts and recordings), waveform processing (transcriptions), creation of the acoustic and language models and the final integration to a fully functional dictation system. The evaluation of this system is also presented. The Logotypografia database, described here, is available by ELRA. 1.