# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 5 Experimental Results and Discussion
### Proposed System Performance  
Table 1 compares the proposed methods to the six benchmark algorithms in terms of the number of model parameters, the number of multiply-accumulate operations (MACs), and the WER achieved on the clean and noisy test files. The noisy signals have been split into three categories: indoor, outdoor, and transportation noise types, where the SNR was randomly sampled between 0 and 30 dB. As can be seen, the first two innovations (row \"Robust DistilHuBERT\") already provide substantial improvement relative to the original DistilHubert model. Overall, relative gains of 33.04%, 31.16%, and 22.40% are obtained for the indoor, outdoor, and transportation noise types, respectively. By incorporating all three proposed innovations (row \"Noise-aware DistilHuBERT\"), a slight decrease in WER is achieved. It can also be observed that the proposed solutions result in compressed models that achieve similar WER across noise-type conditions, suggesting improved robustness to the ambient factors and applicability to the edge conditions. To further explore the benefits of the proposed context-awareness solution, we next focus on the lower SNR conditions, known to be the most impactful for ASR. Table 2 further reports the achieved accuracy for the benchmarks and proposed solutions for only the noisy files corrupted by additive noise ranging from 0 to 10 dB SNR. As can be seen, the gains achieved with the proposed noise-aware solution outperforms the Robust DistilHuBERT model by 6.19%, 4.24%, and 4.62% for the indoor, outdoor, and transportation noise types, respectively. Relative to the original DistilHuBERT, these relative gains are of 48.42%, 46.42%, and 37.85%, respectively. It is important to emphasize that while the noise-aware solution does require the storage of double the number of parameters relative to DistilHuBERT and RobustDistiler, inference time and computation requirements remain  
Figure 5: Scatterplot of inverse SRMR and true RT60. the same, as only one of the two models is used at a time. As such, the achieved gains can still be useful for edge applications involving very noisy conditions. Lastly, Table 3 compares the WERs achieved for the benchmark and proposed solutions for speech signals under reverberant conditions. As can be seen, reverberation is a more challenging distortion to tackle and the WER of the benchmark distilled model is severely degraded. Implementing the first two innovations (Robust DistilHuBERT) allows the WER to be reduced by 66.52% overall and by 88.87% and 55.27% for the high RT60 and low RT60 conditions, respectively, relative to DistilHuBERT. The proposed environment-aware solution further decreases WER by an extra 2.55%,  
2.76%, and 2.87%, respectively. Interestingly, the proposed solutions with only 24 M or 48 M parameters already significantly improve ASR accuracy relative to HuBERT Large with 300 M parameters. For the high reverberation level conditions, for example, the proposed environment-aware solution improves on HuBERT Large by 70.75% while requiring roughly one-sixth of the number of parameters. The results reported above have relied on predicted SNR/RT60 estimates. We have also explored the use of an \"oracle\" system in which the true SNR/RT60 values are used (i.e., assuming perfect classification), but this did not result in any significant improvement, suggesting that the few errors made by the classifiers had minimal impact on the overall recognition accuracy. Overall, the obtained findings show the benefits of the proposed solutions for both noisy and reverberant settings, with the greatest gains seen in extremely low SNR conditions and highly reverberant settings, thus making the proposed models ideal for edge applications.