Discriminative feature domains for reverberant acoustic environments - Papayiannis,C; Evers,C; Naylor,PA (IEEE, 2017)
Several speech processing and audio data-mining applicationsrely on a description of the acoustic environment as a featurevector for classification. The discriminative properties of thefeature domain play a crucial role in the effectiveness of thesemethods. In this work, we consider three environment iden-tification tasks and the task of acoustic model selection forspeech recognition. A set of acoustic parameters and Ma-chine Learning algorithms for feature selection are used andan analysis is performed on the resulting feature domains foreach task. In our experiments, a classification accuracy of100% is achieved for the majority of tasks and the Word Er-ror Rate is reduced by 20.73 percentage points for AutomaticSpeech Recognition when using the resulting domains. Ex-perimental results indicate a significant dissimilarity in theparameter choices for the composition of the domains, whichhighlights the importance of the feature selection process forindividual applications.