Improvement in Language Detection by Neural Discrimination in Comparison with Predictive Models

In this paper, we present a new method of language detection. This method is based on language pair discrimination using neural networks as classifier of acoustic features. No acoustic decomposition of the speech signal is needed. We present an improvement of our method applied to the detection of English for a signal duration of less than 3 seconds (Call Friend corpus), as well as a comparison with a neural predictive model. The obtained results highlight scores ranging from 74.7% to 76.9% according to the method used.