Chongqing dialect speech recognition based on fine-tuning lightweight pretrained models

Abstract

To address the low accuracy and poor adaptation of generic speech recognition models to unique pronunciations and vocabularies in Chongqing dialect scenarios, this paper constructs a multi-scenario and multi-speaker Chongqing dialect speech dataset. Based on the FunASR framework, we fine-tune the lightweight pretrained model SenseVoiceSmall. Four groups of controlled experiments are designed: baseline, SpecAugment augmentation only, domain hotword enhancement only, and their combination. Results show that the joint optimization strategy achieves the best performance: the model’s Average Correctness (Avg Corr) increases from 81.67% to 84.48%, and Average Character Error Rate (Avg CER) decreases from 25.31% to 20.09%. The recognition of colloquial expressions, unique vocabularies, and typical pronunciations of Chongqing dialect is significantly improved. Meanwhile, the model achieves a Real-Time Factor (RTF) of 0.005 with an average inference latency of 0.045 seconds per utterance, demonstrating high efficiency for lightweight deployment. This work provides a lightweight solution for Chongqing dialect speech recognition and a valuable reference for low-resource dialect research.