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Manner of articulation detection using deep neural networks require a prioriknowledge of the attribute discriminative features or the decent phonemealignments. However generating an appropriate phoneme alignment is complex andits performance depends on the choice of optimal number of senones, Gaussians,etc. In the first part of our work, we exploit the manner of articulationdetection using connectionist temporal classification (CTC) which doesn't needany phoneme alignment. Later we modify the state-of-the-art character basedposteriors generated by CTC using the manner of articulation CTC detector. Beamsearch decoding is performed on the modified posteriors and it's impact on opensource datasets such as AN4 and LibriSpeech is observed.

Beam Search Decoding using Manner of Articulation Detection Knowledge Derived from Connectionist Temporal Classification