Advances in deep learning methods for speech recognition and understanding

This work presents several studies in the areas of speech recognition and     understanding.     The semantic speech understanding is an important sub-domain of the     broader field of artificial intelligence.     Speech processing has had interest from the researchers for long time     because language is one of the defining characteristics of a human being.     With the development of neural networks, the domain has seen rapid progress     both in terms of accuracy and human perception.     Another important milestone was achieved with the development of     end-to-end approaches.     Such approaches allow co-adaptation of all the parts of the model     thus increasing the performance, as well as simplifying the training     procedure.     End-to-end models became feasible with the increasing amount of available     data, computational resources, and most importantly with many novel     architectural developments.     Nevertheless, traditional, non end-to-end, approaches are still relevant     for speech processing due to challenging data in noisy environments,     accented speech, and high variety of dialects.      In the first work, we explore the hybrid speech recognition in noisy     environments.     We propose to treat the recognition in the unseen noise condition     as the domain adaptation task.     For this, we use the novel at the time technique of the adversarial     domain adaptation.     In the nutshell, this prior work proposed to train features in such     a way that they are discriminative for the primary task,     but non-discriminative for the secondary task.     This secondary task is constructed to be the domain recognition task.     Thus, the features trained are invariant towards the domain at hand.     In our work, we adopt this technique and modify it for the task of     noisy speech recognition.      In the second work, we develop a general method for regularizing     the generative recurrent networks.     It is known that the recurrent networks frequently have difficulties     staying on same track when generating long outputs.     While it is possible to use bi-directional networks for better     sequence aggregation for feature learning, it is not applicable     for the generative case.     We developed a way improve the consistency of generating long sequences     with recurrent networks.     We propose a way to construct a model similar to bi-directional network.     The key insight is to use a soft L2 loss between the forward and     the backward generative recurrent networks.     We provide experimental evaluation on a multitude of tasks and datasets,     including speech recognition, image captioning, and language modeling.      In the third paper, we investigate the possibility of developing     an end-to-end intent recognizer for spoken language understanding.     The semantic spoken language understanding is an important     step towards developing a human-like artificial intelligence.     We have seen that the end-to-end approaches show high     performance on the tasks including machine translation and speech recognition.     We draw the inspiration from the prior works to develop     an end-to-end system for intent recognition.

Keyphrases: Deep learning, Machine learning, Speech recognition, Neural networks, Domain adaptation, Noisy speech recognition, Adversarial learning, Recurrent neural networks, Sequence generation, Spoken language understanding, End-to-end learning