Research on Speech Token Redundancy: Uncovering Optimization Opportunities in Embedding Layers of Large Language Models

This article introduces the open-source research project speech-token-redundancy, focusing on the redundancy issue in the embedding layers of speech-language models. Key findings include: many speech token embeddings are highly similar and can be merged while maintaining performance to achieve model compression and efficiency optimization, providing new ideas for deployment in resource-constrained scenarios.

With the widespread application of Large Language Models (LLMs) in speech processing, model size and computational cost have become key challenges for practical deployment. As a bridge between audio signals and language models, the representation method of speech tokens directly affects model performance and efficiency. Optimization of embedding layers is an important direction to reduce computational overhead while maintaining model capabilities.

1. Token Embedding Similarity Patterns: Analysis of the embedding space reveals that many token embeddings are highly similar, stemming from the continuity of speech signals and local correlations of acoustic features, leading to repeated computation of similar features.
2. Impact of Redundancy on Performance: The number of independent embeddings can be significantly reduced while maintaining overall model performance, providing a theoretical basis for lightweight speech models.
3. Cross-Layer Redundancy Observation: Repeatedly encoded speech features exist across different model layers, suggesting that architecture can be optimized through feature reuse mechanisms.

The project uses multiple techniques to quantify embedding redundancy:

• Similarity Measurement: Cosine similarity and Euclidean distance are used to quantify the similarity of embedding vectors
• Clustering Analysis: Group similar embeddings and identify token sets that can share representations
• Ablation Experiments: Systematically remove or merge embeddings to evaluate their actual impact on performance
• Visualization Analysis: Use t-SNE and UMAP dimensionality reduction to display the structure of the embedding space

1. Model Compression and Acceleration: Eliminating redundant embeddings reduces parameter count and memory usage, facilitating deployment in resource-constrained environments such as mobile devices and edge nodes.
2. Training Efficiency Improvement: Compact embedding representations reduce parameter updates, accelerate the training process, and lower computational costs.
3. Inspiration for New Architecture Design: Provides directions for efficient architecture strategies such as dynamic embeddings and adaptive tokenization.

Limitations:

• Current analysis is based on specific speech model architectures; universality requires more verification
• The trade-off between embedding redundancy and performance needs to be finely quantified
• Efficient utilization of findings in practical systems requires further exploration

Future Directions: Cross-modal redundancy analysis, dynamic embedding compression algorithms, and optimization strategies for specific application scenarios.

The speech-token-redundancy project reveals significant redundancy in the embedding layers of speech-language models through empirical analysis, opening up new paths for model optimization. It is expected to reduce computational overhead while maintaining performance. As speech AI applications become more widespread, such efficiency optimization research will become increasingly important.