Improvements to deep convolutional neural networks for LVCSR (1309.1501v3)

Abstract: Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural Networks (DNN), as they are able to better reduce spectral variation in the input signal. This has also been confirmed experimentally, with CNNs showing improvements in word error rate (WER) between 4-12% relative compared to DNNs across a variety of LVCSR tasks. In this paper, we describe different methods to further improve CNN performance. First, we conduct a deep analysis comparing limited weight sharing and full weight sharing with state-of-the-art features. Second, we apply various pooling strategies that have shown improvements in computer vision to an LVCSR speech task. Third, we introduce a method to effectively incorporate speaker adaptation, namely fMLLR, into log-mel features. Fourth, we introduce an effective strategy to use dropout during Hessian-free sequence training. We find that with these improvements, particularly with fMLLR and dropout, we are able to achieve an additional 2-3% relative improvement in WER on a 50-hour Broadcast News task over our previous best CNN baseline. On a larger 400-hour BN task, we find an additional 4-5% relative improvement over our previous best CNN baseline.

• The paper introduces novel CNN modifications, revealing that full weight sharing offers similar performance to limited sharing for LVCSR.
• It adapts pooling methods and integrates fMLLR-based speaker adaptation, achieving relative WER improvements of 2-5% on broadcast tasks.
• The study implements fixed dropout masks in Hessian-free training with ReLU, contributing an extra 0.6% reduction in WER.

Improvements to Deep Convolutional Neural Networks for LVCSR: A Synthesis

The paper "Improvements to Deep Convolutional Neural Networks for LVCSR" delineates a series of targeted advancements aimed at refining the efficacy of Deep Convolutional Neural Networks (CNNs) in the domain of Large Vocabulary Continuous Speech Recognition (LVCSR). CNNs are superior to Deep Neural Networks (DNNs) due to their inherent advantage in handling spectral variations in input signals. This paper builds upon these merits by introducing novel modifications and assessing their impact on the Word Error Rate (WER) across speech tasks.

The authors propose a comprehensive exploration of four distinctive methodologies:

1. Weight Sharing Analysis: By conducting an exhaustive comparison between Limited Weight Sharing (LWS) and Full Weight Sharing (FWS), the paper aims to identify the optimal balance for maximizing CNN performance in speech recognition. The experimentation reveals that multiple layers of LWS do not significantly outperform FWS, suggesting that simpler FWS might be preferred given its easier implementation.
2. Pooling Strategy Adaptation: Drawing inspiration from computer vision, various pooling strategies such as stochastic pooling and overlapping pooling are evaluated within the speech tasks. Contrary to their success in vision tasks, these pooling strategies demonstrated minimal improvements in generalization for speech, signifying a domain-specific divergence in response to pooling methods.
3. Speaker Adaptation Integration: A significant contribution is the innovative incorporation of feature-space Maximum Likelihood Linear Regression (fMLLR) with log-mel features. By effectively transforming features to an uncorrelated space prior to applying fMLLR, substantial gains in WER were achieved, highlighting the utility of fMLLR when appropriately implemented in correlated feature spaces tailored for CNNs.
4. Dropout in Hessian-Free Training: Addressing dropout utility in a second-order Hessian-free sequence training context, the authors introduce a mechanism to maintain fixed dropout masks per utterance, enhancing convergence and maintaining dropout benefits. This critical adjustment affirms the role of rectified linear units (ReLU) and dropout in achieving a 0.6% improvement in WER, optimizing CNN performance post cross-entropy training.

Empirical validation of these strategies underscores their effectivity, achieving a 2-3% relative improvement in WER over prior CNN baselines on a 50-hour Broadcast News task, and a further 4-5% on a 400-hour equivalent. This evidences the potential scalability and robustness of the improvements across significantly larger data volumes.

Implications and Future Directions

The advancements delineated have both practical and theoretical ramifications. Practically, the modular improvements promise immediate applicability to LVCSR systems, directly influencing real-world speech recognition systems such as virtual assistants and automated transcription. Theoretically, this research consolidates the understanding of how CNNs can be tailored and adapted for speech recognition, distinct from the methodologies traditionally successful in vision.

Future explorations could delve into synergistic effects between these strategies and emerging architectural novelties like Transformer models. Investigating hybrid architectures or fine-tuning dropout strategies specifically for sequence learning tasks could yield even deeper insights, possibly bridging existing gaps in cross-modal model efficiencies. Such advancements could further democratize the development of effective speech recognition technologies across diverse linguistic and environmental contexts.

In conclusion, this paper exemplifies a methodical approach to augmenting CNN performance for LVCSR, offering a substantiated contribution to the field of speech recognition through strategic enhancements and nuanced methodological adjustments.