# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
## 3 Experimental Setup  
### Data Description  
The idea behind the baseline model training is to obtain a generic model exposed to a huge variety of data, so that an adaptation to new unseen domains can be performed later by transferring that general knowledge to a target dataset. Table 1 summarises datasets used for training and evaluating the baseline SAD system.  
As it can be observed, the baseline SAD is trained on a combination of data coming from three domains: broadcast, telephone channel and meetings. For the broadcast domain, the system is trained on a combination of data from previous Albayzin evaluation campaigns (2010 and 2018) and data from the Multi-Genre broadcast (MGB) challenge. For the meetings domain, AMI and ICSI meetings corpora are used. Finally, in order to represent the telephonic domain, the summed partition of NIST 2008 speaker recognition evaluation (SRE) is incorporated into training. In addition, 10% of each training dataset considered is reserved to generate a validation subset containing data across the three domains considered. As described in Table 1, we also reserve an additional dataset from each domain to evaluate the obtained results in that domain with the baseline SAD system. These datasets are the Albayzin 2020 evaluation partition for broadcast domain, CALLHOME dataset for telephonic domain and the dataset originally released for NIST Rich Transcription (RT) 2009 evaluation for meetings domain.  
The main goal of this work is to adapt SAD models trained on a variety of domains to a new unseen domain. This new domain is the one introduced in the Fearless Steps Challenge, with audio from Apollo mission featuring quite degraded channels and several kinds of transmission noises. In the following lines, we describe partitions provided originally in the second phase of the challenge (FS-02) and explain how they have been used in this work:  
* **Train**: Training subset is made of 125 files of approximately 30 min duration each. This makes a total of approximately 62.5 h of audio. Despite the ground truth labels for this partition are available, in the experiments we consider the target domain unlabelled, so we make no use of those labels in our systems. The audio is used then as target data in order to adapt SAD models to this new unseen domain.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Domain**} \\ \cline{2-4}  & **Broadcast** & **Telephonic** & **Meetings** \\ \hline \multirow{3}{*}{**Train data**} & Albayzin 2010—TV3 [63] & & AMI [64] \\ \cline{2-4}  & Albayzin 2018—RTVE [65] & SRE08 Summed & \\ \cline{2-4}  & MGB [67] & & ICSI Meetings [66] \\ \hline
**Test data** & Albayzin 2020—RTVE [68] & CALLHOME [69] & RT09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data description for baseline SAD training.  
* **Development**: There are available 30 files of 30 min length for development purposes, resulting in around 15 h of audio. In this work, this subset is used to evaluate our systems, in terms of the particular metrics introduced.  
Note that the evaluation partition provided by the organisation was not used in this work. The scoring of this subset was performed by organisers while running the challenge and labels have not been released publicly, making it impossible to obtain comparable results on this subset at the moment of developing this work. Furthermore, all the obtained results in this work for the Fearless Steps data are under the challenge conditions because participants were allowed to use any available data in addition to the data provided by the organisation to train and tune their systems.