On Training Recurrent Networks with Truncated Backpropagation Through Time in Speech Recognition
This work addresses the challenge of understanding and improving long-term dependency learning in recurrent networks for speech recognition, which is incremental as it builds on existing training methods.
The paper investigates how recurrent neural networks learn long-term dependencies in speech recognition by analyzing the impact of decoding approaches like online and batch decoding, and connecting them to truncated backpropagation through time, showing that design choices such as lookahead and context frames characterize network behavior.
Recurrent neural networks have been the dominant models for many speech and language processing tasks. However, we understand little about the behavior and the class of functions recurrent networks can realize. Moreover, the heuristics used during training complicate the analyses. In this paper, we study recurrent networks' ability to learn long-term dependency in the context of speech recognition. We consider two decoding approaches, online and batch decoding, and show the classes of functions to which the decoding approaches correspond. We then draw a connection between batch decoding and a popular training approach for recurrent networks, truncated backpropagation through time. Changing the decoding approach restricts the amount of past history recurrent networks can use for prediction, allowing us to analyze their ability to remember. Empirically, we utilize long-term dependency in subphonetic states, phonemes, and words, and show how the design decisions, such as the decoding approach, lookahead, context frames, and consecutive prediction, characterize the behavior of recurrent networks. Finally, we draw a connection between Markov processes and vanishing gradients. These results have implications for studying the long-term dependency in speech data and how these properties are learned by recurrent networks.