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In this work, we propose an acoustic embedding based approach forrepresentation learning in speech recognition. The proposed approach involvestwo stages comprising of acoustic filterbank learning from raw waveform,followed by modulation filterbank learning. In each stage, a relevanceweighting operation is employed that acts as a feature selection module. Inparticular, the relevance weighting network receives embeddings of the modeloutputs from the previous time instants as feedback. The proposed relevanceweighting scheme allows the respective feature representations to be adaptivelyselected before propagation to the higher layers. The application of theproposed approach for the task of speech recognition on Aurora-4 and CHiME-3datasets gives significant performance improvements over baseline systems onraw waveform signal as well as those based on mel representations (averagerelative improvement of 15% over the mel baseline on Aurora-4 dataset and 7% onCHiME-3 dataset).

Representation Learning For Speech Recognition Using Feedback Based Relevance Weighting