This paper presents a front-end consisting of an Artificial Neural Network (ANN) architecture trained with multilingual corpora. The idea is to train an ANN front-end able to integrate the acoustic variations included in databases collected for different languages, through different channels, or even for specific tasks. This ANN front-end produces discriminant features that can be used as observation vectors for language or task dependent recognizers. The approach has been evaluated on three difficult tasks: recognition of non-native speaker sentences, training of a new language with a limited amount of speech data, and training of a model for car environment using a clean microphone corpus of the target language and data collected in car environment in another language.
Bibliographic reference. Scanzio, Stefano / Laface, Pietro / Fissore, Luciano / Gemello, Roberto / Mana, Franco (2008): "On the use of a multilingual neural network front-end", In INTERSPEECH-2008, 2711-2714.