The MateCat tool

Abstract We present a new web-based CAT tool providing translators with a professional work environment, integrating translation memories, terminology bases, concordancers, and machine translation. The tool is completely developed as open source software and has been already successfully deployed for business, research and education. The MateCat Tool represents today probably the best available open source platform for investigating, integrating, and evaluating under realistic conditions the impact of new machine translation technology on human post-editing

Advances in the Automatic Transcription of Lectures

Transcribing lectures is a challenging task, both in acoustic and in language modeling. In this work, we present recent results on the automatic transcription of lectures from the Translanguage English Database, which contains the recordings of talks given in English at Eurospeech `93, by mostly non-native speakers. Concerning acoustic modeling, the acoustic model trained for a broadcast news transcription task was adapted on the lectures training data through Maximum Likelihood Linear Regression adaptation, including models of spontaneous speech phenomena. Moreover, a normalization procedure was embodied in the training stage, consisting in a cluster-based mean and variance normalization of the static features. Language modeling was based on adaptation of a background language model estimated on broadcast news transcripts, conference proceedings, lecture transcripts, and conversational speech transcripts. Among the examined adaptation techniques, the most effective one was obtained by exploiting the paper presented in each lecture to be processed. The best transcription performance on a 2 hours test set was 32.4% word error rat

A Speech-to-Speech Translation based Interface for Tourism

. This paper presents a speech-to-speech translation system for tourism application developed in the context of the C-STAR consortium. Potential users can communicate by speech and by using their own language with a travel agent in order to organize their travel. The system uses an interchange format representation of the semantic contents of utterances, which is flexible and simplifies the system portability to new languages. A demonstrative prototype, developed at ITC-irst, is now working for the Italian modules and was integrated with the English counter part developed at the Interactive System Laboratory at CMU. 1 Introduction In the field of tourist information, users from every part of the world may want to access an information system to get information for organizing their travels. However, potential users are not computer scientists nor are keen onto use artificial languages for interacting with the system. They would rather exploit their own language. This is why multi-lingu..

A System for the Segmentation and Transcription of Italian Radio News

This paper presents the development of an Italian broadcast news transcription system, to be applied for the indexing of multimedia archives. Moreover, a broadcast news corpus under collection at ITC-irst is introduced. The system processes the input audio stream in four stages. The first one performs audio segmentation via the Bayesian Information Criterion (BIC) and classification by Gaussians mixtures modeling. The second stage groups spectrally homogeneous speech segments, again using the BIC method, in order to provide speaker clusters suitable for the following adaptation module. The third stage adapts the acoustic models to each selected cluster and, finally, the fourth stage transcribes the audio data employing cluster adapted models. The achieved word error rate, measured on a 1h:15m test set, corresponding to 6 news programs, was 21.5%

Cross-Task Portability of a Broadcast News Speech Recognition System

This paper reports on experiments of porting the ITC-irst Italian broadcast news recognition system to two spontaneous dialogue domains. Porting was investigated by applying state-of-the-art adaptation methods on acoustic and language models, and by evaluating the trade-off between performance and required amount of task specific annotated data. The use of different levels of supervision for acoustic model adaptation was also studied. By employing two hours of manually annotated speech, word error rates of 26.0% and 28.4% were achieved by the adapted systems These results are to be compared with the performance of two domain specific baseline systems, 22.6% and 21.2% respectively, which were developed on much more training data. Finally, a robust method is presented that allows to tune the insertion of spontaneous speech phenomena by the speech decode

A Baseline For The Transcription Of Italian Broadcast News

This paper presents the first achievements in the development of a broadcast news transcription system to be applied for the processing of huge audio archives. In particular, the Italian broadcast news corpus under collection is introduced, and the first implemented baseline system is outlined. The baseline system consists of an audio segmentation module and a speech recognizer featuring a recursive Viterbi beam search, a 64K-word lexicon, a treebased trigram LM representation, and MLLR adaptation. The word error rate of the baseline was 20.9% on planned studio speech and 28.8% on the whole test set. 1. INTRODUCTION This work presents a large vocabulary speech recognition system under development at ITC-irst for the transcription of Italian broadcast news. The system will be applied for the processing of huge audio archives of the Italian major broadcasting company. Moreover, the Italian broadcast news corpus under collection at ITC-irst 1 is described. The system performs several..