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