🤖 AI Summary
To address the insufficient accuracy and robustness of the Kaldi ASR system across diverse scenarios, this work proposes three core optimizations: (1) a lightweight Conformer acoustic model integrating multi-stream TDNN-F architecture to enhance time-frequency modeling capability; (2) a dynamic hyperparameter tuning framework based on Bayesian optimization, jointly optimizing acoustic and language model hyperparameters; and (3) a dynamic n-gram language model pruning and caching strategy that balances recognition accuracy and inference efficiency. Experiments demonstrate that the proposed method reduces word error rate by 12.7% on Common Voice and 9.3% on AISHELL-2, significantly mitigating overfitting and improving cross-domain generalization. Moreover, the system supports low-latency, scalable industrial deployment.
📝 Abstract
This technical report introduces innovative optimizations for Kaldi-based Automatic Speech Recognition (ASR) systems, focusing on acoustic model enhancement, hyperparameter tuning, and language model efficiency. We developed a custom Conformer block integrated with a multistream TDNN-F structure, enabling superior feature extraction and temporal modeling. Our approach includes advanced data augmentation techniques and dynamic hyperparameter optimization to boost performance and reduce overfitting. Additionally, we propose robust strategies for language model management, employing Bayesian optimization and $n$-gram pruning to ensure relevance and computational efficiency. These systematic improvements significantly elevate ASR accuracy and robustness, outperforming existing methods and offering a scalable solution for diverse speech recognition scenarios. This report underscores the importance of strategic optimizations in maintaining Kaldi's adaptability and competitiveness in rapidly evolving technological landscapes.