Using multiple visual tandem streams in audio-visual speech recognition

The method which is called the "tandem approach" in speech recognition has been shown to increase performance by using classifier posterior probabilities as observations in a hidden Markov model. We study the effect of using visual tandem features in audio-visual speech recognition using a novel setup which uses multiple classifiers to obtain multiple visual tandem features. We adopt the approach of multi-stream hidden Markov models where visual tandem features from two different classifiers are considered as additional streams in the model. It is shown in our experiments that using multiple visual tandem features improve the recognition accuracy in various noise conditions. In addition, in order to handle asynchrony between audio and visual observations, we employ coupled hidden Markov models and obtain improved performance as compared to the synchronous model

Crossmodal Attentive Skill Learner

This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. We provide concrete examples where the approach not only improves performance in a single task, but accelerates transfer to new tasks. We demonstrate the attention mechanism anticipates and identifies useful latent features, while filtering irrelevant sensor modalities during execution. We modify the Arcade Learning Environment [Bellemare et al., 2013] to support audio queries, and conduct evaluations of crossmodal learning in the Atari 2600 game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017], we open-source a fast hybrid CPU-GPU implementation of CASL.Comment: International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2018, NIPS 2017 Deep Reinforcement Learning Symposiu