Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition (1701.03360v3)

Abstract: In this paper, a novel architecture for a deep recurrent neural network, residual LSTM is introduced. A plain LSTM has an internal memory cell that can learn long term dependencies of sequential data. It also provides a temporal shortcut path to avoid vanishing or exploding gradients in the temporal domain. The residual LSTM provides an additional spatial shortcut path from lower layers for efficient training of deep networks with multiple LSTM layers. Compared with the previous work, highway LSTM, residual LSTM separates a spatial shortcut path with temporal one by using output layers, which can help to avoid a conflict between spatial and temporal-domain gradient flows. Furthermore, residual LSTM reuses the output projection matrix and the output gate of LSTM to control the spatial information flow instead of additional gate networks, which effectively reduces more than 10% of network parameters. An experiment for distant speech recognition on the AMI SDM corpus shows that 10-layer plain and highway LSTM networks presented 13.7% and 6.2% increase in WER over 3-layer aselines, respectively. On the contrary, 10-layer residual LSTM networks provided the lowest WER 41.0%, which corresponds to 3.3% and 2.8% WER reduction over plain and highway LSTM networks, respectively.

• The paper presents a Residual LSTM design that separates spatial and temporal gradient flows to overcome training challenges in deep recurrent networks.
• Methodologically, reusing LSTM components cuts model parameters by over 10%, enhancing efficiency and streamlining network design.
• Empirical results on the AMI Meeting Corpus demonstrate significant word error rate improvements, validating the architecture's effectiveness in distant speech recognition.

Residual LSTM: Advancements in Deep Recurrent Architectures for Speech Recognition

The paper "Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech Recognition," authored by Jaeyoung Kim, Mostafa El-Khamy, and Jungwon Lee, presents a novel approach in the recurrent neural network (RNN) domain, specifically focusing on Long Short-Term Memory (LSTM) networks. The central contribution of this work is the introduction of the Residual LSTM architecture, which aims to address the known challenges in training deep recurrent networks, notably the vanishing and exploding gradient problems. This innovation proposes a dual-path strategy, separating spatial and temporal gradient flows to enhance training robustness and reduce network parameter counts.

Technical Overview

The LSTM, equipped with memory cells and gating mechanisms, is traditionally utilized for learning long-term dependencies in sequential data. The introduction of a residual architecture to LSTMs represents a significant development. The paper delineates the key components of the Residual LSTM:

• Spatial Shortcut Path: Diverging from the highway LSTM that utilizes memory cells for shortcut connections, the Residual LSTM employs output layers as shortcut paths. This separation of pathways minimizes the interference in gradient flow, thus enhancing the model's ability to learn efficiently with depth.
• Reuse of LSTM Components: The approach recycles the output projection matrix and gates as part of the spatial shortcut mechanism, leading to over 10% reduction in parameters compared to previous LSTM extensions like the highway LSTM.

Empirical Results

The paper presents comprehensive experimental results on the AMI Meeting Corpus, showing the impact of different LSTM architectures on word error rate (WER) in distant speech recognition tasks. Key findings include:

• Comparison with Baseline Models: The 10-layer Residual LSTM architecture significantly outperforms both plain LSTM and highway LSTM configurations, achieving a WER of 41.0%. This translates to a 3.3% and 2.8% WER reduction compared to plain and highway LSTMs, respectively.
• Training Dynamics: Residual LSTM exhibited better cross-validation performance without training loss as network depth increased, contrary to highway LSTM, which degraded at higher depths.
• Parameter Efficiency: The reuse of existing LSTM components contributed to the streamlined model design with a reduction in parameter count, demonstrating efficient resource utilization.

Theoretical and Practical Implications

The separation of spatial and temporal pathways in Residual LSTM extends theoretical insights into architecture designs capable of mitigating gradient issues prevalent in deep models. Practically, the efficient realization of such architectures could enhance automatic speech recognition systems' performance, particularly in challenging environments like distant and noisy settings.

Future Directions

This work opens several avenues for future research. The architectural principles of Residual LSTM may be adapted to other recurrent units beyond LSTM, such as GRUs, potentially widening the scope of efficacy. Additionally, exploring the synergy between Residual LSTM and attention mechanisms could further bolster sequence-to-sequence modeling tasks. Lastly, the practical deployment in real-world speech recognition applications could be pursued to quantify benefits beyond controlled experimental settings.

In conclusion, the Residual LSTM represents a methodical enhancement of deep recurrent architectures for speech recognition. By introducing novel connection paradigms and optimizing network complexity, the paper contributes meaningfully to the ongoing development of efficient, scalable neural network models.