## 3 Methods
### Corpus Database  
Due to the difficulties for obtaining labels for the stress vs neutral conditions with [PERSON] we did not record audios using [PERSON]'s system and microphone for this research. We chose the VOCE Corpus Database [20] instead which was not originally designed for speaker identification. There are several reasons why this database was chosen: first, for having data taken in real stress conditions; second, for offering data from sensors similar to those present in [PERSON]'s bracelet from which heart rate measurements could be obtained; and third, due to the existence of previous studies [7] confirming the feasibility of relating heart rate metrics with stress in speech.  
The last updated version of this dataset includes a total of 135 voice recordings that result from a set of 45 students (21 men, 17 women and 7 unidentified) from the University of Porto, with ages between 19 and 49 years. These voice files correspond to three different recording settings: _pre-baseline_, reading a standard text at least 24 h before the event in public (Public Speaking, PS); _baseline_, reading the same text as in the _pre-baseline_ setting approximately 30 min before the PS; and _recording_, free speaking from a public event of free duration.  
Figure 3: R peaks in ECG signal [39].  
Figure 2: Block diagram of the system.  
Together with these audio files, 117 files containing 2 measured physiological variables are provided and used to estimate the Heart Rate (HR). These measurements, taken with a Zephyr HxM BT2 device, are (i) \(Z_{\textit{xcg}}\) representing an averaged and filtered HR value with a sampling period of 1 s. and (ii) \(Z_{\textit{ts}}\) values that refer to the instants of time in which R peaks (shown in Figure 3) occur in the electrocardiogram obtained with the device, measured with an internal clock of 16 bits. Each of these values is accompanied by the Universal Time Coordinated (UTC) corresponding moment.  
Furthermore, the database contains a metadata file that includes gender, age, health information, experience in public speaking, STAI (State-Trait Anxiety Inventory) [40] test scores and information about the quality of the recordings (energy level, saturation...). Unfortunately, this is only provided for 38 out of the 45 individuals in the database and the database only gathers complete information (the 3 audio files and its corresponding HR values) from 21 individuals.  
We decided to divide these 21 speakers into two sets, _Set 1_ was composed of 10 speakers whose HR was coherent with the recordings in the sense that, when a speaker was reading the heart rate remained stable, but on the public speaking setting the HR rose significantly. _Set 2_ was made out of the other 11 remaining speakers.  
As for our specific application, the emotional conditions perceived in speech during an assault situation to be detected by Bindi--such as panic, anxiety or fear--, are not the same to stress conditions caused by public speech, but it seems to a recognizable prior condition that can lead to more intense emotions. As further work, we aim at using data specifically captured in these kinds of situations in the future.