ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context (2005.03191v3)

Abstract: Convolutional neural networks (CNN) have shown promising results for end-to-end speech recognition, albeit still behind other state-of-the-art methods in performance. In this paper, we study how to bridge this gap and go beyond with a novel CNN-RNN-transducer architecture, which we call ContextNet. ContextNet features a fully convolutional encoder that incorporates global context information into convolution layers by adding squeeze-and-excitation modules. In addition, we propose a simple scaling method that scales the widths of ContextNet that achieves good trade-off between computation and accuracy. We demonstrate that on the widely used LibriSpeech benchmark, ContextNet achieves a word error rate (WER) of 2.1%/4.6% without external LLM (LM), 1.9%/4.1% with LM and 2.9%/7.0% with only 10M parameters on the clean/noisy LibriSpeech test sets. This compares to the previous best published system of 2.0%/4.6% with LM and 3.9%/11.3% with 20M parameters. The superiority of the proposed ContextNet model is also verified on a much larger internal dataset.

• The paper presents ContextNet, a novel CNN-RNN transducer that integrates SE modules to incorporate global context into ASR.
• It introduces a scalable parameter tuning strategy and replaces ReLU with Swish activation to lower word error rates on Librispeech.
• Empirical evaluations show that ContextNet outperforms prior CNN-based models by achieving a WER as low as 2.1% on the clean test set without an external language model.

An Overview of ContextNet: Enhancing Convolutional Neural Networks for Speech Recognition

The paper under discussion presents ContextNet, a novel CNN-RNN-transducer architecture that significantly advances the state-of-the-art in end-to-end automatic speech recognition (ASR). ContextNet is designed with a fully convolutional encoder that leverages global context information through the integration of squeeze-and-excitation (SE) modules, thereby addressing the traditionally limited context handling capability of CNNs in the domain of ASR.

Architectural Innovations

ContextNet introduces several pertinent innovations aimed at improving ASR performance using CNNs:

1. Global Context Incorporation: A pivotal element of ContextNet is the use of SE modules that integrate global context into CNNs. These modules execute a global average pooling operation, squeezing local feature maps into a context vector that modulates the response of the convolutional layers, effectively emulating the extensive temporal context available in RNN/Transformer-based architectures.
2. Efficient Parameter Scaling: The authors propose a model scaling strategy that adjusts the network's width, allowing a flexible trade-off between computation and accuracy. This is crucial for deploying ASR systems across varying resource environments.
3. Use of Swish Activation: Incorporating the Swish activation function instead of the conventional ReLU is reported to yield a consistent reduction in word error rate (WER), contributing to the model's effectiveness.

Empirical Evaluation

ContextNet was rigorously evaluated on the Librispeech benchmark. Several configurations of ContextNet demonstrated:

• Achieving a WER of 2.1% on the clean test set and 4.6% on the noisy test set without an external LLM (LM). Incorporating an LM improved the WER to 1.9% and 4.1%, respectively.
• ContextNet with only 10.8M parameters achieved a WER of 2.9%/7.0%, outperforming previous CNN-based models like QuartzNet, which with 20M parameters, had a WER of 3.9%/11.3%.

Such results underscore the efficacy of ContextNet in reducing the performance gap between CNN-based ASR models and those based on RNN or Transformer architectures.

Implications and Future Directions

The integration of SE modules in ContextNet suggests a promising direction for CNN-based architectures to better exploit global contextual information, which has been underutilized in traditional convolutional setups. This emphasizes the potential of convolutional models to rival or even surpass other architectures typically favored for ASR tasks, especially in scenarios where model simplicity and parameter efficiency are key considerations.

The scalability of ContextNet, achieved through parameter tuning and a progressive downsampling strategy, also hints at broader applicability beyond ASR. Future research could explore its deployment in other sequence processing tasks, such as natural language processing or time-series prediction, where context integration and efficient computation remain critical.

Furthermore, as developments in ASR continue, integrating these findings with ongoing advancements in architecture design, such as hybrid models combining the strengths of CNNs, RNNs, and Transformers, could yield even more robust systems. These innovations would not only improve accuracy but could also lower the cost of computational resources, making high-quality ASR more accessible across diverse applications.