0000000000862890
AUTHOR
M. Inés Torres
Speech-input multi-target machine translation
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
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.