End-to-end acoustic modeling using convolutional neural networks for HMM-based automatic speech recognition.

•Novel CNN-based end-to-end acoustic modeling approach is proposed.•Relevant features are automatically learned from the signal by discriminating phones.•Learned features are more discriminative than cepstral-based features.•Learned features are somewhat invariant to languages and domains.•Proposed approach leads to better ASR systems.