  Multi-task learning (MTL) involves the simultaneous training of two or more
related tasks over shared representations. In this work, we apply MTL to
audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn
a mapping between audio-visual fused features and frame labels obtained from
acoustic GMM/HMM model. This is combined with an auxiliary task which maps
visual features to frame labels obtained from a separate visual GMM/HMM model.
The MTL model is tested at various levels of babble noise and the results are
compared with a base-line hybrid DNN-HMM AV-ASR model. Our results indicate
that MTL is especially useful at higher level of noise. Compared to base-line,
upto 7\% relative improvement in WER is reported at -3 SNR dB
