Search results for "speech translation"
showing 4 items of 4 documents
Towards the evaluation of automatic simultaneous speech translation from a communicative perspective
2021
In recent years, automatic speech-to-speech and speech-to-text translation has gained momentum thanks to advances in artificial intelligence, especially in the domains of speech recognition and machine translation. The quality of such applications is commonly tested with automatic metrics, such as BLEU, primarily with the goal of assessing improvements of releases or in the context of evaluation campaigns. However, little is known about how the output of such systems is perceived by end users or how they compare to human performances in similar communicative tasks. In this paper, we present the results of an experiment aimed at evaluating the quality of a real-time speech translation engine…
Data Augmentation for Pipeline-Based Speech Translation
2020
International audience; Pipeline-based speech translation methods may suffer from errors found in speech recognition system output. Therefore, it is crucial that machine translation systems are trained to be robust against such noise. In this paper, we propose two methods for parallel data augmentation for pipeline-based speech translation system development. The first method utilises a speech processing workflow to introduce errors and the second method generates commonly found suffix errors using a rule-based method. We show that the methods in combination allow significantly improving speech translation quality by 1.87 BLEU points over a baseline system.
Speech-input multi-target machine translation
2007
In order to simultaneously translate speech into multiple languages an extension of stochastic finite-state transducers is proposed. In this approach the speech translation model consists of a single network where acoustic models (in the input) and the multilingual model (in the output) are embedded. The multi-target model has been evaluated in a practical situation, and the results have been compared with those obtained using several mono-target models. Experimental results show that the multi-target one requires less amount of memory. In addition, a single decoding is enough to get the speech translated into multiple languages.
An integrated architecture for speech-input multi-target machine translation
2007
The aim of this work is to show the ability of finite-state transducers to simultaneously translate speech into multiple languages. Our proposal deals with an extension of stochastic finite-state transducers that can produce more than one output at the same time. These kind of devices offer great versatility for the integration with other finite-state devices such as acoustic models in order to produce a speech translation system. This proposal has been evaluated in a practical situation, and its results have been compared with those obtained using a standard mono-target speech transducer.