HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition (2305.18281v1)

Abstract: State-of-the-art ASR systems have achieved promising results by modeling local and global interactions separately. While the former can be computed efficiently, global interactions are usually modeled via attention mechanisms, which are expensive for long input sequences. Here, we address this by extending HyperMixer, an efficient alternative to attention exhibiting linear complexity, to the Conformer architecture for speech recognition, leading to HyperConformer. In particular, multi-head HyperConformer achieves comparable or higher recognition performance while being more efficient than Conformer in terms of inference speed, memory, parameter count, and available training data. HyperConformer achieves a word error rate of 2.9% on Librispeech test-clean with less than 8M neural parameters and a peak memory during training of 5.7GB, hence trainable with accessible hardware. Encoder speed is between 38% on mid-length speech and 56% on long speech faster than an equivalent Conformer. (The HyperConformer recipe is publicly available in: https://github.com/speechbrain/speechbrain/tree/develop/recipes/LibriSpeech/ASR/transformer/)

Overview of HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition

The paper "HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition" presents an advancement in Automatic Speech Recognition (ASR) system architectures by introducing HyperConformer. This model extends the capabilities of the Conformer architecture by incorporating the efficient HyperMixer module—a promising alternative to attention mechanisms—thereby reducing computational overhead while maintaining or improving performance metrics.

Methodological Contributions

The authors address the inefficiencies associated with attention mechanisms, particularly their quadratic complexity, by integrating HyperMixer into the Conformer architecture. The novel HyperConformer architecture replaces the traditional Multi-Head Self-Attention (MHSA) with Multi-head HyperMixer, optimizing for both global interaction capture and computational efficiency.

Key components of the HyperConformer include:

1. Token Mixing Techniques: Utilizes HyperMixer, which generates token mixing Multi-Layer Perceptrons (MLP) through hypernetworks, achieving linear complexity in processing.
2. Multi-head Token Mixing: Implements parallel token mixing heads to enhance efficiency comparable to the multi-head approach in traditional attention-based models.
3. Convolution Modules: Ensures the capture of local interactions, maintaining the strengths of the original Conformer design.

Experimental Results

The paper undertakes comprehensive experimentation on the LibriSpeech dataset, demonstrating that HyperConformer achieves a word error rate (WER) of 2.9% on the test-clean dataset with only 8M parameters. The HyperConformer model exhibits:

• Improved Efficiency: Achieving a reduction in processing time by 37% to 56% on mid-length to long speech sequences compared to Conformer.
• Reduced Memory Usage: Up to 30% less memory consumption during training.
• Comparable or Superior Accuracy: Maintains performance on par with or better than existing Conformer models.

Implications and Future Directions

The introduction of HyperConformer within ASR systems poses significant implications for both practical deployment and the broader research community:

• Resource Accessibility: Facilities training and deployment on more accessible computational resources without sacrificing performance.
• Model Scalability: Offers potential scalability and application to other domains where sequence length can impact computational demands significantly.

Future research directions may involve exploring further optimizations in token mixing efficiency and expanding the application of HyperConformer beyond speech recognition, potentially benefiting other sequence-based tasks such as natural language processing and time-series analysis.

Conclusion

HyperConformer represents a notable innovation in ASR model design, providing a pathway to more efficient yet powerful architectures by leveraging HyperMixer's strengths. The results underline the potential of moving beyond traditional attention mechanisms towards more sustainable and resource-efficient computation models, particularly in domains reliant on long input sequences.