Light Gated Recurrent Units for Speech Recognition (1803.10225v1)

Abstract: A field that has directly benefited from the recent advances in deep learning is Automatic Speech Recognition (ASR). Despite the great achievements of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially in challenging environments characterized by significant noise and reverberation. To improve robustness, modern speech recognizers often employ acoustic models based on Recurrent Neural Networks (RNNs), that are naturally able to exploit large time contexts and long-term speech modulations. It is thus of great interest to continue the study of proper techniques for improving the effectiveness of RNNs in processing speech signals. In this paper, we revise one of the most popular RNN models, namely Gated Recurrent Units (GRUs), and propose a simplified architecture that turned out to be very effective for ASR. The contribution of this work is two-fold: First, we analyze the role played by the reset gate, showing that a significant redundancy with the update gate occurs. As a result, we propose to remove the former from the GRU design, leading to a more efficient and compact single-gate model. Second, we propose to replace hyperbolic tangent with ReLU activations. This variation couples well with batch normalization and could help the model learn long-term dependencies without numerical issues. Results show that the proposed architecture, called Light GRU (Li-GRU), not only reduces the per-epoch training time by more than 30% over a standard GRU, but also consistently improves the recognition accuracy across different tasks, input features, noisy conditions, as well as across different ASR paradigms, ranging from standard DNN-HMM speech recognizers to end-to-end CTC models.

• The paper introduces Li-GRU, a simplified GRU architecture that removes the reset gate to enhance computational efficiency in speech recognition.
• It employs ReLU activations and batch normalization, resulting in over 30% reduction in training time per epoch while maintaining accuracy.
• Empirical tests demonstrate that Li-GRU consistently improves recognition accuracy across various datasets and challenging acoustic conditions.

Analysis of "Light Gated Recurrent Units for Speech Recognition"

The research paper titled "Light Gated Recurrent Units for Speech Recognition," authored by Mirco Ravanelli, Philemon Brakel, Maurizio Omologo, and Yoshua Bengio, presents a substantial contribution to the field of Automatic Speech Recognition (ASR) by proposing a simplified Recurrent Neural Network (RNN) architecture called Light Gated Recurrent Units (Li-GRU). This paper is particularly relevant for researchers focused on deep learning applications in speech processing.

The primary innovations of the Li-GRU architecture involve the removal of the reset gate from the standard Gated Recurrent Unit (GRU) design, known for its two multiplicative gates: the update and reset gates. The authors identify redundancy in the functional roles of these gates, specifically positing that in the context of ASR, especially in challenging acoustic environments, the reset gate does not significantly contribute to performance. Therefore, they argue for a streamlined GRU that is both computationally cheaper and more effective by only retaining the update gate.

Furthermore, Li-GRU adopts Rectified Linear Unit (ReLU) activations in place of the hyperbolic tangent functions typically used in GRUs. This adjustment is purported to improve learning efficiency by facilitating better gradient flow, benefiting from the well-established advantages of ReLU in mitigating the vanishing gradient problem, particularly in feed-forward networks. Coupled with batch normalization, these modifications enhance the model’s ability to learn long-term dependencies without encountering numerical stability issues common with ReLU activations.

Empirical validation presents compelling evidence for the optimized ASR performance of Li-GRU. The architecture demonstrates improvements over a range of datasets and acoustic conditions, including close-talking (e.g., TIMIT) and distant-talking environments (e.g., DIRHA English WSJ, CHiME 4), utilizing both hybrid (DNN-HMM) and end-to-end (CTC) ASR frameworks. Key numerical results underline over 30% reduction in per-epoch training time compared to standard GRUs alongside consistent improvements in recognition accuracy across different feature sets and noisy conditions.

The paper contributes to the theoretical understanding of modality-specific RNN architectures by highlighting the task-specific nature of gating mechanisms within GRUs. The authors further provide detailed gradient and gate correlation analyses, underpinning their architectural choices and supporting the theoretical soundness of their design simplifications.

Practically, the proposed model's reduced computational load suggests potential applications in small-footprint ASR systems, important for deploying ASR technology in resource-limited settings, such as mobile and embedded devices. Moreover, the architectural simplicity might also translate into more efficient training processes and scalable deployment in extensive, diverse ASR tasks.

Future work may involve extending Li-GRU's application to other domains needing sequence modeling and exploring additional synergies with network regularization and architectural innovations. The adaptability of similar streamlined RNN structures in tackling diverse sequence prediction problems beyond speech recognition remains a fertile ground for investigation.

The researchers provide a thorough body of work that not only tests the revised architecture across multiple datasets but also engages in a foundational exploration of GRU dynamics within the specific use case of speech recognition, reinforcing the importance of domain-tailored neural network innovations.