# TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants +
## 4 Results
However, the best results are obtained with the TASE-E2E case, where the SE models and the classifiers are trained jointly using all three losses. We compare the WUW detection results of TASE-E2E with other state-of-the-art SE models (SEGAN [27] and Denoiser [30]), followed by a classifier (data augmented LeNet)  
Figure 5: F1-score box plot for different SNR ranges. Classifiers trained with a very wide range of noise (\([-10,50]\) dB SNR). Figure 6: Comparison of different training methods for the SE models and LeNet classifier, in terms of the macro F1-Score for different SNR ranges. All models trained in the range of \([-10,50]\) dB SNR. in different noise scenarios. In Table 5, it can be observed that, when training the models together with the task loss, the results in our setup are better than with other, more powerful, but more general, SE models. We hypothesize that this is due to the natural adaptation of the SE to the classifier during the end-to-end training, as well as having been trained with a focus on common home noises. TASE-E2E improves the detection over the no SE model case, especially in scenarios with background conversations, loud office noise, or loud TV; see Table 6.  
Table 7 shows that our SE system does not improve speech quality with respect to the case where we do not use any model to enhance speech. This was expected because our models have not been trained to remove generic background noises that the Valentini dataset contains. Instead, we train an SE system to learn to remove background conversations and TV noise that could trigger the device, which can lead to speech deterioration for the Valentini data. Nevertheless, we observe that PESQ improves in the case of TASE coupled with a LeNet classifier compared with SE, and the best results are obtained in the end-to-end case, where the PESQ and STOI results obtained without an SE module are preserved. This demonstrates that the consideration of the classification task in the loss improves the capacity of the SE model to clean speech. when compared to a standalone WUW classifier that has been trained on a wide SNR range between 50 and \(-\)10 dBs. In that case, the results between applying TASE or not using it are on par, with TASE having a slight advantage in the most severely noisy setups, that is, between \(-\)5 and \(-\)10 dBs SNR. Thus, we have reported the potential of TASE at improving the performance of standard neural net classifiers that are not specifically trained to be resilient to noise, and we encourage further experiments in the comparison between speech enhancement and noise data augmentation techniques. Finally, as we have corroborated our hypotheses with a manually segmented corpus, we motivate further work for the online streaming case, with the aim to explore the particularities and challenges that may arise in such a setup. Conceptualization, J.L. ; methodology, D.B., J.L., C.S., F.L. and G.C. ; software, F.L., D.B. and G.C. ; validation, F.L., D.B. and G.C. ; formal analysis, D.B., J.L., F.L. and G.C. ; investigation, D.B., J.L., F.L. and G.C. ; resources, J.L., M.F. and P.G. ; data curation, G.C., J.L., M.F., D.B. and F.L. ; writing--original draft preparation, G.C., F.L., D.B. and J.L. ; writing--review and editing, G.C., F.L., D.B., J.L., M.F., C.S. and P.G. ; visualization, F.L., D.B., G.C. and J.L. ; supervision, J.L. ; project administration, J.L. and P.G. ; funding acquisition, J.L., P.G. and M.F. All authors have read and agreed to the published version of the manuscript. This research was funded by both the INGENIOUS and ACCORDION projects within the European Union's Horizon 2020 Research and Innovation Program under grant numbers 833435 and 871793, respectively. The sixth author has been funded by the Agencia Estatal de Investigacion (AEI), Ministerio de Ciencia, Innovacion y Universidades and the Fondo Social Europeo (FSE) grant number RYC-2015-17239 (AEI/FSE, UE).