# TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants +
###### Abstract
For instance, more recent works have introduced systems based on convolutional [13], recurrent, [14; 15; 16] and self-attention networks [17]. Dealing with robustness and generalization for previous architectures, we can find a widespread strategy based on synthesizing training data by noise augmentation. Noise data augmentation techniques exploit a deep neural network's appetite for vast amounts of data. They artificially corrupt the original samples, which usually translates into better performance figures, making the models to be more robust with regard to a bigger variety of noises or unseen scenarios. Similar approaches can be seen across different speech tasks, such as in keyword spotting [18], in ASR [19; 20], or in WUW detection [21]. Therefore, we adopt similar ideas for our training data employed by all the classifiers described in this work. We artificially mix training samples with additive noise or by creating different kind of artifacts on the original speech, translating into similar findings on performance for our WUW task than those reported in previous works and for other speech tasks. Classical SE methods, such as Wiener filtering [22], spectral subtraction [9], or subspace algorithms [23], specialize in characterizing noise, so it can be reduced from the speech signal. However, such methods do not provide a robust performance against certain contaminations, such as non-stationary noises or overlapped speech. This gap has been addressed in the last few years with deep learning approaches, with some of them acting at the spectral level [12; 24] and others directly at the waveform input signal [25; 26]. One widespread architecture is the encoder-decoder-autoencoder. It can be found in Reference [27], which additionally proposes the generative adversarial network paradigm [28] and makes use of skip connections in the style of U-Net [29]. Another popular model is proposed in Reference [30], which operates at the waveform level, using a similar architecture but including a LSTM [31] between the encoder and the decoder for hidden state sequence-to-sequence modeling. Nevertheless, many current approaches are commonly optimized by minimizing a regression loss in time, or by a combination with a spectrogram domain loss [24; 30]. Motivated by the performance of such models, we propose to study the application and the effects of SE modules and techniques upon the performance of a WUW detection task, extending our previous work in such matter [1]. We hypothesize that cleaning noisy speech with a dedicated SE front-end should be beneficial for a WUW detector. Aiming to validate the previous assumption, we cover different experimental scenarios in this work:  
1. The isolated classifier: just a WUW classifier is available, which is our baseline with no SE. 2. The independent SE and WUW models: both systems are trained separately, thus training the SE model exclusively on waveform and spectral regression losses. 3. The Task-Aware SE (TASE) through frozen WUW model: WUW model is trained beforehand, so it is plugged after the SE model during SE training. This way, the WUW detection logits are available, so the classification loss can be back-propagated at the SE model and summed up to the regression losses. The WUW detector is frozen at SE training. 4. The end-to-end TASE (TASE-E2E) and WUW model training: both systems are jointly trained at the same time from scratch, optimizing the SE model with joint regression and classification losses. Wrapping up, one of the main novelties on the present paper is the study of SE applied to WUW detection, which has not been reported in previous works, to the best of our knowledge. Furthermore, we propose a new loss that makes the SE model task-aware, enhancing the speech in order to maximize the performance later on at the WUW detection stage.