# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 1 Introduction  
Speech Activity Detection (SAD) aims to determine whether an audio signal contains speech or not, and its exact location in the signal. This constitutes an essential preprocessing step in several speech-related applications such as speech and speaker recognition, as well as speech enhancement. In many cases, SAD is used as a preliminary block to separate the segments of the signal that contain speech from those that are only noise. This way, enabling the overall system to process only the speech segments. A large number of approaches have been proposed for the SAD task. Traditionally, statistical approaches have been used with relevant results under the assumption of quasi-stationary noise. Several works rely on the extraction of specific acoustic features [1, 2]. Conversely, other methods are model-based [3, 4], aiming to estimate a statistical model for the noisy signal. Additionally, some unsupervised approaches can also be cited: based on energy [5], or based on the estimation of the signal long-term spectral divergence [6]. Recently, deep learning approaches are becoming increasingly relevant in the SAD task. The research presented in [7] implements a SAD system based on a multilayer perceptron with energy efficiency as the main concern. A deep neural network (DNN) approach is used in [8] to perform SAD in a multi-room environment. In [9], new optimisation techniques based on the area under the ROC curve are explored in the framework of a deep learning SAD system. Recurrent neural networks (RNN) are significantly useful when dealing with temporal sequences of information because they are able to model temporal dependencies introducing a feedback loop between the input and output of the neural network. Several applications of long short-term memory (LSTM) networks [10] can be cited in the SAD task [11, 12]. Some of our latest solutions for SAD in the context of diarisation applicationsobtained competitive results applying a bidirectional LSTM-based classifier [13; 14]. Convolutional Recurrent Neural Networks (CRNN) combine the capability of convolutional networks to capture frequency and time dependencies simultaneously seeking to extract discriminative features, and the capability of recurrent networks to deal with temporal series. A number of examples of the use of CRNN models in audio processing can be found in the literature [15; 16]. CRNN models have been also applied to the SAD task with relevant results [17]. In the last few years, a number of international evaluation campaigns have been proposing the SAD task as one of their challenges, seeking to advance this kind of systems in a variety of challenging domains. In this context, the National Institute of Standards and Technology (NIST) introduced the OpenSAT evaluations starting in 2017 [18]. Three domains were proposed for the SAD task: public safety communications, low resource languages and audio extracted from YouTube videos. Post-evaluations analysis revealed a significant difference in performance among the three domains for most participant teams. Similarly, aiming to motivate the research effort on a demanding domain such as audio from Apollo space missions, a series of annual challenges has been held since 2019 [19] proposing the SAD task among other speech related tasks. This initiative has resulted in the digitisation of the original analogue recordings from the space missions. Part of this data has been made available through the Fearless steps (FS) corpus, consisting of a cumulative 19,000 h of conversational speech coming from the Apollo 11 mission [20]. Audio data belong to 30 different communication channels, with multiple speakers in diverse locations. Most channels show a strong degradation with transmission noise or noise due to tape ageing. Whereas current SAD state-of-the-art solutions rely on the use of deep learning techniques, these applications depend strongly on the amount of labelled data available.