# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
###### Abstract  
Recent advancements in end-to-end (E2E) modeling have notably improved automatic speech recognition (ASR) systems; however, far-field speech recognition (FSR) remains challenging due to signal degradation from factors such as low signal-to-noise ratio, reverberation, and interfering sounds. This requires richer training data and multi-channel speech enhancement. To address this gap, we introduce Mixer, a novel data augmentation technique designed to further enhance the performance of large-scale pre-trained models for FSR. Mixer interpolates and mixes feature representations of speech samples and their corresponding losses, extending the MixSpeech framework to intermediate layers of Whisper. Additionally, we propose Mixer-C, which further leverages multi-channel information by combining speech from different microphone channels using a channel selector. Experimental results demonstrate that Mixer significantly outperforms existing methods, including SpecAugment, achieving a relative word error rate (WER) reduction of 3.6% compared to the baseline. Furthermore, Mixer-C offers an additional WER improvement of 2.2%, showcasing its efficacy in improving FSR accuracy. far-field ASR; data augmentation; mixer; whisper +
Footnote †: journal: Applied sciences  
0000-0001-5000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-000  
Data augmentation (DA) strategies are crucial as they can generate virtually unlimited training samples [4]. Notably, SpecAugment (SA) [5] has demonstrated significant improvements in the accuracy and noise robustness of end-to-end (E2E) ASR systems. However, to our knowledge, relatively few studies have applied data augmentation techniques to multi-channel FSR. Existing attempts primarily include speed/volume perturbation [6], white noise injection [7], and channel dropout [8], indicating further innovation potential in this area. In response to this gap, we propose Mixer, a method that effectively enhances the performance of large-scale pre-trained models for far-field speech recognition. Mixer achieves this by interpolating and mixing feature representations from speech samples and their corresponding losses. According to the MixSpeech paper given in [9], it only mixes the input speech, while Mixer is actually a generalization of MixSpeech. The Mixer method can not only mix the input speech but also mix the intermediate layer features output via the Whisper encoder, thereby offering a more robust framework for FSR. Furthermore, since far-field speech recognition employs multiple microphone arrays to capture sound, single-channel data may be incomplete due to factors such as speaker position and movement. To enhance multi-channel speech, we further propose Mixer-C, which selects and combines speech from different channels with the same label through a channel selector. This approach strengthens the multi-channel information of FSR, enabling a more comprehensive understanding for speech and thereby enhancing performance. Additionally, by mixing different channels of speech, Mixer-C addresses a significant limitation that relies on the same array configuration during both training and testing, improving the model's adaptation to various array configurations. Although large-scale pre-trained models have demonstrated exceptional performance across various tasks, the immense parameter size of these models makes fine-tuning computationally expensive and prone to overfitting. Therefore, we employ the LoRA [10] algorithm to investigate the performance of FSR on Whisper as our baseline. Our main contributions are as follows: (a) We explore efficient fine-tuning methods based on Whisper for far-field speech recognition (FSR). (b) We introduce a data augmentation method, Mixer, specifically designed for FSR, which significantly enhances model performance. (c) We present Mixer-C, an extension of Mixer, to further leverage multi-channel information for improved accuracy.