Towards an Automatic Dictation System for Translators: the TransTalk Project

Professional translators often dictate their translations orally and have them typed afterwards. The TransTalk project aims at automating the second part of this process. Its originality as a dictation system lies in the fact that both the acoustic signal produced by the translator and the source text under translation are made available to the system. Probable translations of the source text can be predicted and these predictions used to help the speech recognition system in its lexical choices. We present the results of the first prototype, which show a marked improvement in the performance of the speech recognition task when translation predictions are taken into account.Comment: Published in proceedings of the International Conference on Spoken Language Processing (ICSLP) 94. 4 pages, uuencoded compressed latex source with 4 postscript figure

Laser-assisted synthesis of superparamagnetic Fe@Au core-shell nanoparticles

Peer reviewed: YesNRC publication: Ye

Hidden Markov models, maximum mutual information estimation, and the speech recognition problem

Hidden Markov Models (HMMs) are one of the most powerful speech recognition tools available today. Even so, the inadequacies of HMMs as a "correct" modeling framework for speech are well known. In that context, we argue that the maximum mutual information estimation (MMIE) formulation for training is more appropriate vis-a-vis maximum likelihood estimation (MLE) for reducing the error rate. We also show how MMIE paves the way for new training possibilities.We introduce Corrective MMIE training, a very efficient new training algorithm which uses a modified version of a discrete reestimation formula recently proposed by Gopalakrishnan et al. We propose reestimation formulas for the case of diagonal Gaussian densities, experimentally demonstrate their convergence properties, and integrate them into our training algorithm. In a connected digit recognition task, MMIE consistently improves the recognition performance of our recognizer

French Speech Recognition in an Automatic Dictation System for Translators: the TransTalk Project

This paper describes a system designed for use by professional translators that enables them to dictate their translation. Because the speech recognizer has access to the source text as well as the spoken translation, a statistical translation model can guide recognition. This can be done in many different ways---which is best? We discuss the experiments that led to integration of the translation model in a way that improves both speed and performance. 1 Introduction The TransTalk project attempts to integrate speech recognition and machine translation in a way that makes maximal use of their complementary strengths. Professional translators often dictate their translations first and have them typed afterwards. If they dictate to a speech recognition system instead, and if that system has access to the source language text, it can use probabilistic translation models to aid recognition. For instance, if the speech recognition system is deciding between the acoustically similar French ..