EfficientASR: A Lightweight Speech Recognition Model with Reduced Attention Redundancy and Optimized Feedforward Networks

Grunnleggende konsepter

EfficientASR employs Shared Residual Multi-Head Attention (SRMHA) to reduce redundant attention computations and Chunk-Level Feedforward Networks (CFFN) to decrease the number of parameters, resulting in a lightweight and versatile speech recognition model.

Sammendrag

The paper introduces EfficientASR, an efficient and lightweight speech recognition model that addresses the high computational and storage requirements of Transformer-based models.

Key highlights:

1. Shared Residual Multi-Head Attention (SRMHA) module:

   • Reduces redundant attention computations by sharing attention scores across layers through residual connections.
   • Integrates low-level and high-level attention distributions to enhance feature fusion.
   • Applies sliding window with deformability (SWD) to further reduce redundancy in single-layer attention maps.

2. Chunk-Level Feedforward Networks (CFFN):

   • Divides the feedforward network into multiple chunks based on embedding dimensions.
   • Uses smaller feedforward networks in each chunk to substantially reduce the number of learnable parameters without compromising model performance.

3. Experimental results:

   • On the Aishell-1 dataset, EfficientASR achieves a 36% reduction in parameters compared to the baseline Transformer model, with a 0.3% improvement in Character Error Rate (CER).
   • On the HKUST dataset, EfficientASR reduces the parameter count by 36% while maintaining a 0.2% improvement in CER over the Transformer baseline.
   • The Conformer EfficientASR model further reduces the parameter count by 38% compared to the Conformer model, with a test set CER of 4.9%.

The proposed EfficientASR model effectively addresses the computational and storage challenges of Transformer-based speech recognition models, making them more versatile and deployable on resource-constrained devices.

Statistikk

The EfficientASR model has 19.33M parameters, which is a 36% reduction compared to the baseline Transformer model.
The EfficientASR model achieved a 0.3% reduction in Character Error Rate (CER) on the Aishell-1 test set compared to the Transformer baseline.
The EfficientASR model achieved a 0.2% reduction in CER on the HKUST test set compared to the Transformer baseline.
The Conformer EfficientASR model reduced the parameter count by 38% compared to the Conformer model while maintaining a test set CER of 4.9%.

Sitater

"EfficientASR employs two primary modules: Shared Residual Multi-Head Attention (SRMHA) and Chunk-Level Feedforward Networks (CFFN)."
"The SRMHA module effectively reduces redundant computations in the network, while the CFFN module captures spatial knowledge and reduces the number of parameters."

Viktige innsikter hentet fra

EfficientASR: Speech Recognition Network Compression via Attention Redundancy and Chunk-Level FFN Optimization

by Jianzong Wan... klokken arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19214.pdf

Dypere Spørsmål

How can the EfficientASR model be further optimized to achieve even greater computational and storage efficiency without compromising speech recognition performance

To further optimize the EfficientASR model for enhanced computational and storage efficiency without compromising speech recognition performance, several strategies can be implemented. One approach is to explore more advanced quantization techniques to reduce the precision of the model parameters, thereby decreasing the memory footprint without significantly impacting accuracy. Additionally, employing pruning methods to eliminate redundant or less important connections in the network can further reduce the model size while maintaining performance.
Another optimization avenue is to investigate knowledge distillation techniques, where a smaller, more efficient model is trained to mimic the behavior of the larger EfficientASR model. By transferring knowledge from the larger model to the smaller one, the overall computational and storage requirements can be significantly reduced. Furthermore, exploring novel architectural modifications, such as hierarchical attention mechanisms or adaptive chunking strategies, could help streamline the model's operations and improve efficiency.

What are the potential limitations or trade-offs of the SRMHA and CFFN techniques, and how could they be addressed in future research

While SRMHA and CFFN techniques offer significant benefits in reducing attention redundancy and parameter count, there are potential limitations and trade-offs to consider. One limitation of SRMHA is that it may introduce additional computational overhead due to the need for updating attention scores and managing residual connections. This could impact the overall efficiency of the model, especially in scenarios with limited computational resources. To address this, optimizing the implementation of SRMHA through parallel processing or efficient memory management could mitigate these challenges.
Similarly, CFFN may face trade-offs in terms of capturing complex spatial knowledge and maintaining model performance. Dividing the feed-forward networks into chunks could potentially limit the network's ability to learn intricate feature representations across the entire input sequence. To overcome this, exploring adaptive chunking strategies or incorporating dynamic chunk selection mechanisms based on input complexity could help mitigate these limitations and enhance the model's performance.
In future research, addressing these limitations could involve conducting in-depth analyses of the computational costs and performance trade-offs associated with SRMHA and CFFN. Additionally, exploring hybrid approaches that combine these techniques with other lightweighting methods could offer a more balanced solution for optimizing speech recognition models.

How could the EfficientASR model be adapted or extended to other speech-related tasks, such as speech enhancement or speaker recognition, and what challenges might arise in those applications

Adapting the EfficientASR model to other speech-related tasks, such as speech enhancement or speaker recognition, presents both opportunities and challenges. For speech enhancement, the EfficientASR model could be extended by incorporating additional audio processing modules, such as denoising or audio source separation, to improve the quality of input speech signals. By integrating these modules with the existing EfficientASR architecture, the model could effectively enhance speech signals before recognition, leading to improved accuracy and robustness in noisy environments.
In the context of speaker recognition, the EfficientASR model could be adapted by incorporating speaker embeddings or speaker-specific features into the network architecture. By training the model to not only recognize speech content but also identify speaker characteristics, the model could be used for speaker verification or identification tasks. However, challenges may arise in maintaining a balance between speech recognition performance and speaker recognition accuracy, as the addition of speaker-related features could introduce complexity and potentially impact overall model efficiency.
Addressing these challenges would require careful design considerations, such as optimizing the model architecture to accommodate both speech and speaker information effectively. Additionally, leveraging transfer learning techniques or multi-task learning approaches could help the model learn to extract relevant features for both speech recognition and speaker recognition tasks simultaneously. By addressing these challenges, the EfficientASR model could be successfully extended to various speech-related applications with improved performance and versatility.