# TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants +
## 3 Materials and Methods
### Wake-Up Word Detection Models  
With the purpose of measuring the quality of the task-aware SE models, we report the impact of the SE module on WUW detection performance using several trigger word detection models. Typically, the end device that runs the WUW detector model has constrained capabilities; thus, the forward delay is a relevant parameter to consider while selecting the architecture of the audio classifier. Bigger models tend to perform better but may lead to an undesired delay in the detection, propagating this delay to the whole conversational chain and, consequently, degrading the user experience.  
As baseline classifier, a LeNet[45] is used, a well-known convolutional neural network (CNN) composed of two convolution layers with ReLU activations and two pooling layers, followed by a final dense block consisting of two fully-connected layers.  
Additionally, based on the work of [PERSON] and [PERSON] [13], which consists of the exploration of lightweight CNNs for keyword spotting, both limiting the number of operations and the number of parameters, and the [PERSON] and [PERSON]'s re-implementation in PyTorch [46], we use the cnn-trad-pool2 architecture. This model consists of two convolutional layers, each one followed by pooling in time and frequency. [PERSON] and [PERSON] also worked with deep residual networks combined with dilated convolutions [47], obtaining comparable results with other CNN-based architectures and giving the possibility to vary the depth and width to achieve small footprint architectures. From this work we use resnet15, resnet15-narrow, and resnet8, which have 15, 15, and 8 ResNet blocks and 45, 19, and 45 feature maps, respectively.  
To continue with, we also use two RNN-based models based on the open source tool named Mycroft Precise [48], which is a lightweight WUW detection tool implemented in TensorFlow. Named as SGRU and SGRU2, these are two bigger variations that we have implemented in PyTorch. The first one has a single GRU with a hidden size of 200, and the second one has two GRUs with a hidden size of 100.  
Finally, we adapt an architecture from a kernel [49] in Kaggle's FAT 2019 competition [50], named as CNN-FAT2019, which has shown good performance in tasks, such as audio tagging or detection of gender, identity, and speech events from pulse signal [51]. This is the biggest architecture used, and it is conformed by eight convolutional layers with ReLU activations, with pooling layers every two convolutional layers.  
In Table 4, we present the number of parameters, operations (multiplications and additions), and the size of every keyword detection architecture used. RNN-based networks are the smallest, and ResNet-based architectures show the variability of operations and parameters, depending on the depth and width.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **Parameters** & **Operations (mult. and add.)** & **Size (MB)** \\ \hline lenet & 4.7M & 21M & 19.2 \\ cnn-trad-pool2 & 183k & 42M & 2.23 \\ resnet15 & 237.4k & 1433M & 29.96 \\ resnet15-narrow & 42.4k & 256M & 12.44 \\ resnets & 109k & 57M & 3.55 \\ sgru & 145.6k & 144.4k & 0.81 \\ sgru2 & 103.4k & 102.2k & 0.53 \\ cnn-fat2019 & 5.2M & 1218M & 41.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters and number of operations of WUW detection models.