# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 4 Experiments
### Results
Variations in audio signals, such as slight environmental changes or differences in speaker characteristics, can lead to significant changes in feature values, causing the method to fail in detecting speaker changes. BIC-GMM appears less influenced by variability of different feature value, it achieves more stable performance across different meetings. It also maintains more balanced FAR and MDR rates across all features. Table 5 reports the average results of meetings for different features. It can be observed that the lowest MDR value of 44.99% was achieved by BIC-GMM with the MFCC feature, with a corresponding FAR of 48.37%. KL-GMM with the MFCC feature gave an MDR of 46.71% and an FAR of 54.28%, which is considered a good performance compared to other features. These results indicate that MFCC features is good fit to SCD problem when using KL and BIC techniques. Additionally, the conditional probability of speaker change given a feature change, \(P(SC|FC)_{MFCC}\), is 53.40% for the MFCC feature (see Table 1). This value aligns with the experimental result for both BIC-GMM and KL-GMM with MFCC feature. The conditional probability for the Chroma feature, \(P(SC|FC)_{Chroma}\), is 51.49%, showing a similar pattern. When we calculate the standard deviation for KL divergence and BIC-GMM model, it can be observed that, for KL divergence (0.29 for FAR, 0.28 for MDR), its std values are much higher than BIC-GMM's (0.011 for FAR, 0.029 for MDR). This indicates that the BIC-GMM model has more consistent performance across different instances, with lower variability in both FAR and MDR. In contrast, the higher standard deviation values for KL divergence suggest that its performance fluctuates more depending on the specific audio data and conditions. Figure 4 presents the relation between maximum probability of speaker change given feature change Max\(P(SC|FC)\) with the FAR and MDR results for BIC-GMM and KL-GMM. To compare different features, we need to compare both MDR and FAR value in a balanced way. For example, in BIC-GMM with feature \(RMS\) (see Figure 4a,b), its FAR value is lowest among others, but it has high MDR. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **FAR** & **MDR** \\ \hline naive\({}_{0}\) & 0 & 1 \\ \hline naive\({}_{1}\) & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of naive systems for comparison. It can be observed that for MFCC in BIC-GMM (see Figure 3(a),b), the highest probability of \(P(SC|FC)\) corresponds to the lowest MDR and FAR values. It shows Mel-spectrogram has the highest value for both MDR and FAR scores in BIC-GMM (see Figure 3(a),b) It may indicate it is not suits to the SCD compared to others. To clearly demonstrate the effectiveness of different features on two distinct methods, we use a combined score of MDR and FAR by approximating the F1 score:  
\[\text{Approx. F1}\approx 2\times\frac{(1-\text{FAR})\times(1-\text{MDR})}{(1-\text{FAR })+(1-\text{MDR})} \tag{8}\]  
Figure 5 compares the performance of various audio features across two speaker change detection methods: KL-GMM and BIC-GMM, using approx. F1 scores, and shows the relation with Max\(P(SC|FC)\). In both methods, MFCC achieving the highest approx. F1 score, which indicates effective detection with a balanced reduction in both false alarms and missed detection. Correspondingly, its Max\(P(SC|FC)\) value is also the highest.