Skip to content

“Discriminative feature domains for reverberant acoustic environments”

IEEE Xplore Access:

in Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)


C. Papayiannis, C. Evers, and P. A. Naylor


Several speech processing and audio data-mining applications rely on a description of the acoustic environment as a feature vector for classification. The discriminative properties of the feature domain play a crucial role in the effectiveness of these methods. In this work, we consider three environment identification tasks and the task of acoustic model selection for speech recognition. A set of acoustic parameters and Machine Learning algorithms for feature selection are used and an analysis is performed on the resulting feature domains for each task. In our experiments, a classification accuracy of 100% is achieved for the majority of tasks and the Word Error Rate is reduced by 20.73 percentage points for Automatic Speech Recognition when using the resulting domains. Experimental results indicate a significant dissimilarity in the parameter choices for the composition of the domains, which highlights the importance of the feature selection process for individual applications.
Published inConferencesPublications

Be First to Comment

Leave a Reply

Your email address will not be published. Required fields are marked *