# TASE: Task-Aware Speech Enhancement for Wake-Up Word Detection in Voice Assistants +
###### Abstract  
Wake-up word spotting in noisy environments is a critical task for an excellent user experience with voice assistants. Unwanted activation of the device is often due to the presence of noises coming from background conversations, TVs, or other domestic appliances. In this work, we propose the use of a speech enhancement convolutional autoencoder, coupled with on-device keyword spotting, aimed at improving the trigger word detection in noisy environments. The end-to-end system learns by optimizing a linear combination of losses: a reconstruction-based loss, both at the log-mel spectrogram and at the waveform level, as well as a specific task loss that accounts for the cross-entropy error reported along the keyword spotting detection. We experiment with several neural network classifiers and report that deeply coupling the speech enhancement together with a wake-up word detector, e.g., by jointly training them, significantly improves the performance in the noisiest conditions. Additionally, we introduce a new publicly available speech database recorded for the Telefonica's voice assistant, Aura. The OK Aura Wake-up Word Dataset incorporates rich metadata, such as speaker demographics or room conditions, and comprises hard negative examples that were studiously selected to present different levels of phonetic similarity with respect to the trigger words \"OK Aura\". speech enhancement; wake-up word; keyword spotting; deep learning; convolutional neural network +
Footnote †: This work is an extended version of our previous paper (2020) paper. coming from the S2T interface. Furthermore, S2T modules tend to be highly complex, computationally expensive, and, most of the time, prohibitive for low-resourced or embedded devices. They are required to operate under both highly variable and noisy scenarios and, consequently, they are often specifically fine-tuned to efficiently tackle the diversity of vocabulary size, prosody, or background noises, among others, within a specific language domain. With the aim of avoiding such an excessive usage of resources along the inference stage, it is common to require the pronunciation of a wake-up word (WUW), which triggers the S2T functionality and the rest of conversational mechanisms. The WUW module is only supposed to discern between the trigger word itself and any other kind of acoustic input, thus becoming a two-class hypothesis test, or verification step, that translates into a less computationally and resource demanding system than an always-awake S2T model. Despite its simplicity with respect to a large vocabulary automatic speech recognizer, the WUW model still needs to be robust enough to handle acoustic distractions, such as TV, music, or overlapping speech. Noisy environments impact the WUW's performance both by waking-up unexpectedly, that translates into false alarm errors, and by not properly catching the trigger word, also known as miss errors. Those errors, especially false alarms, dramatically impact the user experience and reduce the user's expectations on the technology and his/her engagement with it. Therefore, there are common approaches employed for improving robustness in WUW detection. Some of them are based on a second-step verification, typically an automatic speech recognition (ASR) model or a WUW model [2; 3; 4; 5]. Other works incorporate a Speech Enhancement (SE) module that employs a dedicated stage, at the audio input, aiming to reduce noise and to obtain an improved version of the acoustic signal. SE tackles the task of improving the perceptual intelligibility and quality of speech by usually removing background noises [6; 7]. Although it is typically applied for a better perceptual experience in telecommunications [8] and hearing aids [9], SE has also reported improved results, e.g., as a pre-processing step into the context of ASR systems [10; 11; 12]. With respect to the WUW detection and keyword spotting tasks themselves, recent approaches have reported on the benefits of using the most advanced deep learning architectures.