# TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants +
## 4 Results  
Figure 2 shows an example of speech enhanced spectrogram using the TASE architecture. The audio combines background music with the keyword between \(1.15\) and \(1.95\) s, andthe enhanced audio completely preserves the speech relevant information for the posterior classification. Figure 3a-d show the behavior of TASE when plugged to some of the different WUW classifiers described in Section 3.3. We find that TASE is notably beneficial to models, such as SGRU or cnn-trad-pool2, which present lowest robustness to noise, as compared to ResNet15 or CNN-FAT2019, where TASE yields equal or worse performance at some noise ranges. We hypothesize that ResNet15 and CNN-FAT2019 do not benefit of the speech enhancement as much, since they are bigger and more complex architectures that already handle the nuances of noise with more precision. However, an exhaustive fine-tuning of hyperparameters has not been done for every architecture, as we have prioritized covering more models, instead of deeply fine-tuning a few ones, due to computational restrictions. Therefore, we do not discard that our default hyperparameter choice might be biased toward certain architectures, yielding worse performances for cases, such as the TASE-E2E in ResNet15. Detailed metrics for SGRU and cnn-trad-pool2 can be found in Tables A1 and A2. Furthermore, Figure 4 shows the improvement of the WUW detection in noisy scenarios by concatenating our TASE model with the remaining classifiers described in Section 3.3 that are neither large nor robust to noise (SGRU2, ResNet8, ResNet15-narrow), plus LeNet, which architecture has not been fine-tuned for audio tasks. Classifiers are trained with low noise (\([5,30]\) dB SNR), to simulate a voice assistant system that has not been exposed to overwhelming amounts of noise at training. Applying SE in quiet scenarios maintains fairly good results and, especially, improves the models in lower SNR ranges. Nonetheless, in the case when the classifiers are trained with a wider SNR range (\([-10,50]\) dB SNR) by data augmentation, the performance gap between using TASE or not using is significantly reduced. F1-scores between both choices are on par for most of the SNR ranges. The noisiest range of \([-5,-10]\) dB SNR shows a small advantage for the model with respect to TASE, but it is not as large as the averaged improvement reported in Figure 4; see Figure 5.  
Figure 2: Example of Speech Enhancement spectrograms. Each figure shows (**a**) a noisy log-mel spectrogram and (**b**) an enhanced log-mel spectrogram. The blue rectangle shows where the “OK Aura” keyword is placed. Figure 4: F1-score box plot for different SNR ranges. Classifiers trained with a limited range of noise (\([5,30]\) dB SNR). Figure 3: WUW detection performance comparison for different models in terms of F1-score, with and without TASE. All models are trained in the range of \([-10,50]\) dB SNR. TASE is not beneficial in noisy scenarios for large architectures (bottom row), while it does contribute positively to smaller models, especially when trained jointly end-to-end (upper row). (**a**) SGRU. (**b**) cnn-trad-pool2. (**c**) CNN-FAT2019. (**d**) ResNet15. In Section 3.4, we have defined the parameters of the loss function (1) to train a classifier (see case (a) in Section 1 list), and three different approaches to train the SE model: standalone (b) or coupled with the classifier (c, d). In Figure 6, we analyze all the cases using a LeNet as WUW detector. We see how TASE-E2E performs better than all the other cases in almost every SNR range. From 40 dB to 10 dB of SNR, the results are very similar for the 4 models. In contrast, in the noisiest ranges the classifiers without SE model are the worst performers, followed by the separate SE case where only the waveform and spectral reconstruction losses are used. We find that the TASE case, which includes the classification loss in the training stage, improves the results for the WUW detection task.