## 2 Experimental Methodology
### Audio Feature Extraction  
#### 2.2.1 File-Level Audio Features  
File-level features (or high-resolution features) were extracted from entire audio files provided (minus the interviewer speech). These include features extracted using various configuration files of the OpenSMILE library (Embobase, Embose-Large, eGeMAPS) and our original features: Speech/Silence statistics and Energy-Time plots. OpenSMILE [48] (open-source speech and music interpretation by large-scale extraction) is a convenient toolkit which provides acoustic feature extraction and is capable of extracting low-level descriptors (LLD). * **Embose**: We extracted 988 acoustic features: 26 LLDs, along with their deltas and their 19 functionals [48; 49]. The feature set contains the mel-frequency cepstral coefficients (MFCCs), voice quality, fundamental frequency (\(f_{0}\)), \(f_{0}\) envelope, line spectral pairs (LSP), intensity features along with their first- and second-order derivatives and several statistical functions applied to these features, resulting in a total of 988 features for every speech segment. * **Embose-Large**: Using Embose-Large [48; 49], we extracted 6552 file-level features: 56 LLDs along with their 56 delta for their 56 delta-delta and 39 functionals. Local features or low-level descriptors (MFCCs, pitch, energy, quality of voice, etc. ), their \begin{table}
\begin{tabular}{l l l} \hline \hline  & **File-Level** & **Frame-Level** \\ \hline Audio & eGeMAPS & OpenSMILE (Prosody) \\  & Embose-Large & VGG \\  & Embose & OpenL3 \\  & Speech/Silence & \\  & Energy-Time Plots & \\ \hline Text & Key of Text Analytics & Word Embedding \\  & Summed Word Embedding & BERT, RoBERTa, DistilBERT \\  & Key of Text & XLNet \\  & Analytics-Extended & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Training features used in our 16 models. first and second derivatives (i.e., delta and delta-delta) and statistical functionals (global features) were extracted. * **eGeMAPS**: We extracted 88 file-level features (25 LLDs and their functionals) using the eGeMAPS [50] configuration in the OpenSMILE toolkit. The eGeMAPS configuration offers a reduced fundamental feature set to include 88 features, comprising of \(f_{0}\) semitone, loudness, spectral flux, MFCCs, jitter, shimmer, F1, F2, F3, alpha ratio, Hammarberg index and their most common statistical functionals [48] and can detect physiological changes in voice production. * **Speech/Silence**: Pauses were identified in the preprocessed audio using a voice activity detection (VAD) algorithm [51]. Speech segments of less than 0.3 s and silence segments of less than 0.2 s are considered false detections and are converted into the opposite type. The feature set consists of nine features. Out of the nine features, five correspond to the number of pauses, which were organized into five \"pause bins\" (pb). Each pause bin is associated with a duration: \(<\)0.5 s (pb1), 0.5-1 s (pb2), 1-2 s (pb3), 2-4 s (pb4) and \(>\)4 s (pb5). The sixth feature is the \"Centre of Mass of pause bins\" (pbcm), where pbcm represents the number of pauses in a bin multiplied by the respective bin number, calculated as a weighted sum of the five bins, as shown in Equation (1). \[pbcm=1*pb1+2*pb2+...+5*pb5\] (1) Furthermore, the remaining three features are \"sprat\" (speech chunk to pause chunk ratio), where the ratio of lengths of each pair of speech chunks and consequent pause chunks are calculated. This speech-silence subsequent segment pair duration ratio in the first, second (median) and third quartiles across the whole recording (spratq1, spratq2 and spratq3) followed by average duration of speech segments were then calculated. Figure 1 shows a typical distribution of normalized Speech/Silence features analyzed. As seen in this Figure 1, the pause bins of the healthy control (CN) group is always lower than the AD group. As expected, the opposite trend of higher speech chunk to pause chunk ratio was observed for spratq features in the CN group.