# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 5 Results  
### Main Results  
We compare Mixer with four different configurations: (1) direct testing performance of the pre-trained model; (2) models fine-tuned using the DiPCo dataset; (3) models trained using LoRA without Mixer (Baseline); and (4) models trained with SpecAugment, an effective data augmentation technique. Given our limited computational resources, we did not proceed with fine-tuning the entire Whisper-Large-v2 or Whisper-Large-v3 models. The experiment results are shown in Table 1. Firstly, it can be seen that the direct testing performance of Whisper is already commendable. Surprisingly, both the Whisper-Medium and Whisper-Small achieved comparable results to the Whisper-Large-v2. This is likely attributed to the presence of the DiPCo dataset in the training corpus of Whisper. Furthermore, owing to the vast capacity of the large model, Whisper-Large-v2's performance on the DiPCo dataset actually declined, whereas Whisper-Large-v2 achieved the best results. This underscores that a combination of large model capacity and improvedtraining still yields exceptional outcomes. Secondly, it can be seen that fine-tuning the full model actually reduced Whisper-Small and Whisper-Medium's performances on the test set. This was primarily due to the substantial distribution discrepancy between the training and test data, causing the model to forget the original DiPCo data while overfitting to the current batch of training data. Thus, the limitations of fine-tuning the entire model become apparent. Therefore, we employ the LoRA method for training, and for large models, it only requires fine-tuning of 2.21 M parameters. Initially, we observed that despite the Whisper-Small model's underwhelming performance, it manages to mitigate the overfitting issue to some degree. Compared with other models, none exhibited overt overfitting, suggesting that the pre-trained initialization of Whisper-Small possesses limited capacity. For Whisper-Medium, utilizing efficient fine-tuning strategies effectively curbs the overfitting that can occur with full model fine-tuning, enabling effective learning. Furthermore, the Whisper-Large-v3 model surpasses the Whisper-Medium model, demonstrating its ability to acquire more knowledge and perform better, demonstrating the Whisper-Large-v3 model's superiority. Moreover, we compared the performance of the SpecAugment and Mixer data augmentation methods. Since the speech domain is different from images, there are few mixing-based data augmentation methods like CutMix [17] other than MixSpeech. Therefore, we compared our method with SpecAugment, a very classical data augmentation method in speech. We found that SpecAugment only achieved some performance improvements on the medium and Whisper-Large-v2 versions, but had no effect on Whisper-Small and Whisper-Large-v3. This indicates that it is difficult for SpecAugment to be effective when it comes to far-field speech recognition data with high noise and complex channel conditions. However, Mixer achieves performance improvements on all models and can achieve greater performance gains compared to SpecAugment. We analyzed that this is mainly because SpecAugment injects noise into the data through time-domain or frequency-domain masking to enhance the data, while the channel environment for far-field speech recognition is already very poor, and injecting noise will affect the model's learning of the data. In contrast, Mixer fits the data distribution in a high-dimensional space through input and output interpolation, thereby enhancing the model's learning. For the Mixer-C method, it can be observed that it is effective on most versions of the models. It performs well on the Whisper-Large-v3 model, surpassing the performance of Mixer. However, it performs worse than Mixer on other versions of the models. We analyze this is closely related to the fundamental capabilities of the baseline model. The enhancement of multi-channel information is more effective in models with strong baseline