# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 3 Experimental Setup  
### Implementation Details  
#### 3.1.1 Feature Extraction Configuration  
The baseline Mel-filterbank features consist of 23-dimensional coefficients extracted using a 25 ms window with a 10 ms frame shift. Following standard practice in EEND architectures, these features are contextualized through a stacking operation that combines 15 consecutive frames (7 frames on each side), resulting in a 345-dimensional vector sampled every 100 ms after applying a subsampling factor of 10.  
For speaker embeddings, we extract 512-dimensional x-vectors using the ECAPAP-TDNN architecture with a 1-s window and 100 ms shift. This configuration, establishedthrough prior experimentation [33], optimizes the trade-off between temporal resolution and speaker discriminative power. During preprocessing, silence segments identified through Oracle VAD (which uses reference labels to precisely identify non-speech regions and replace corresponding embeddings with zeros during both training and inference) are replaced with zero vectors to address the ECAPA-TDNN's limitations in representing non-speech regions, as explored also in [33]. As demonstrated in our previous work, while Oracle VAD is used in these experiments for controlled comparison, a practical implementation could employ an external VAD system (such as [PERSON]'s VAD [35]) during inference to perform this replacement, with final performance depending on the accuracy of that detection system. We do not perform stacking of these embeddings, as each individual x-vector already incorporates sufficient temporal context, and their high dimensionality makes additional stacking unnecessary. When concatenated with 345-dimensional MFB features, this produces a final feature vector of 857 dimensions (\(512+345\)).  
The paralinguistic parameter sets, GeMAPS and eGeMAPS, are extracted using a 60 ms analysis window with 10 ms shift. We did not select a smaller window size due to inherent limitations in the minimum window size required for these feature sets, with 60 ms being the minimum possible. The base GeMAPS set produces 62 features encompassing frequency, energy, spectral, and voice quality parameters, while eGeMAPS extends this to 88 features with additional voice quality and spectral characteristics. When combining features through concatenation, temporal alignment is maintained through consistent 100 ms frame intervals. The resulting feature vector dimension depends on the context size, following the formula: \(Vector_{size}=GeMAPS_{dimension}\times(2\times context+1)\). For example, with our proposed reduced set of 52 GeMAPS features presented in Section 4.1.1 and a context size of 2, the resulting vector has 260 dimensions (52 \(\times\) (2 \(\times\) 2 + 1)). When concatenated with 345-dimensional MFB features, this produces a final feature vector of 605 dimensions. In the results section, we present an analysis for two-speaker scenarios examining how different stacking contexts affect these feature types. Additionally, we explore the use of Oracle VAD during both training and inference for two-speaker cases. However, as our results demonstrate, GeMAPS already contain sufficient information about non-speech segments, making Oracle VAD unnecessary for the multi-speaker experiments.