Federated Heterogeneous Language Model Optimization for Hybrid Automatic Speech Recognition
arXiv:2603.04945v1 Announce Type: new Abstract: Training automatic speech recognition (ASR) models increasingly relies on decentralized federated learning to ensure data privacy and accessibility, producing multiple local models that require effective merging. In hybrid ASR systems, while acoustic models can be merged using established methods, the language model (LM) for rescoring the N-best speech recognition list faces challenges due to the heterogeneity of non-neural n-gram models and neural network models. This paper proposes a heterogeneous LM optimization task and introduces a match-and-merge paradigm with two algorithms: the Genetic Match-and-Merge Algorithm (GMMA), using genetic operations to evolve and pair LMs, and the Reinforced Match-and-Merge Algorithm (RMMA), leveraging reinforcement learning for efficient convergence. Experiments on seven OpenSLR datasets show RMMA achieves the lowest average Character Error Rate and better generalization than baselines, converging up
arXiv:2603.04945v1 Announce Type: new Abstract: Training automatic speech recognition (ASR) models increasingly relies on decentralized federated learning to ensure data privacy and accessibility, producing multiple local models that require effective merging. In hybrid ASR systems, while acoustic models can be merged using established methods, the language model (LM) for rescoring the N-best speech recognition list faces challenges due to the heterogeneity of non-neural n-gram models and neural network models. This paper proposes a heterogeneous LM optimization task and introduces a match-and-merge paradigm with two algorithms: the Genetic Match-and-Merge Algorithm (GMMA), using genetic operations to evolve and pair LMs, and the Reinforced Match-and-Merge Algorithm (RMMA), leveraging reinforcement learning for efficient convergence. Experiments on seven OpenSLR datasets show RMMA achieves the lowest average Character Error Rate and better generalization than baselines, converging up to seven times faster than GMMA, highlighting the paradigm's potential for scalable, privacy-preserving ASR systems.
Executive Summary
This article proposes a novel approach to optimizing language models in hybrid automatic speech recognition (ASR) systems, particularly in decentralized federated learning environments. The authors introduce a match-and-merge paradigm, incorporating two algorithms: the Genetic Match-and-Merge Algorithm (GMMA) and the Reinforced Match-and-Merge Algorithm (RMMA). Experimental results on seven OpenSLR datasets demonstrate RMMA's superiority in achieving lower Character Error Rates and faster convergence compared to GMMA and baseline methods. This work has significant implications for developing scalable and privacy-preserving ASR systems, which are critical for widespread adoption in various industries. The authors' innovative approach to addressing the heterogeneity of language models is a significant contribution to the field of ASR research.
Key Points
- ▸ Decentralized federated learning for ASR model training ensures data privacy and accessibility.
- ▸ Heterogeneous language models pose challenges in hybrid ASR systems.
- ▸ The match-and-merge paradigm is introduced for optimizing language models.
- ▸ The Genetic Match-and-Merge Algorithm (GMMA) and Reinforced Match-and-Merge Algorithm (RMMA) are proposed.
Merits
Strength
The match-and-merge paradigm provides a novel solution to the heterogeneity challenge in language models, potentially leading to more accurate and efficient ASR systems.
Innovative Approach
The incorporation of genetic and reinforcement learning techniques in RMMA offers a unique and effective method for optimizing language models in decentralized environments.
Demerits
Limitation
The experimental results may not be generalizable to all ASR systems and datasets, highlighting the need for further testing and validation.
Scalability
The scalability of the proposed approach in large-scale decentralized environments is not thoroughly explored, which may limit its practical applications.
Expert Commentary
The article presents a significant contribution to the field of ASR research, addressing a critical challenge in decentralized federated learning environments. The proposed match-and-merge paradigm and RMMA algorithm demonstrate promising results, offering a potential solution for optimizing language models in hybrid ASR systems. However, further research is necessary to thoroughly explore the scalability and generalizability of the approach. The implications of this work are substantial, with potential applications in various industries and significant implications for data protection regulations and standards.
Recommendations
- ✓ Future research should focus on exploring the scalability of the proposed approach in large-scale decentralized environments and evaluating its performance on a broader range of ASR datasets.
- ✓ The authors should investigate the potential applications of the match-and-merge paradigm in other domains beyond ASR, such as machine translation and text summarization.