# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 4 Experiments
### Results  
In the threshold optimization process for SCD, Bayesian optimization was applied to determine the optimal threshold for each combination of audio feature for KL-GMM, with the aim of maximizing the performance score on the validation set. The optimization process is configured with the following parameters:  
* Threshold parameter is explored within the range of 0 to 10, allowing the optimizer to identify both highly sensitive and more robust threshold values across various feature and metric combinations. * Initialization points set to 5, this parameter specifies the number of random points the optimizer initially samples to gain a broad understanding of the threshold's impact on the combined metric as peformance score. * Iterations set to 25, this parameter specifies the number of optimization steps following the initial exploration. The process begins with a set number of random samples, after which the optimizer uses these observations to inform subsequent iterations. B focusing on the most promising thresholds, Bayesian optimization quickly converges on the values that maximize the performance score. It is shown in Figure 3, the fluctuation is process of optimization, and with the blue cure, the convergence curve with performance score is plotted. The final optimized threshold values are list in Table 3. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Feature** & **Optimal Threshold** & **Performance Score** \\ \hline Pitch & 5.999179 & 0.528707 \\ MFCC & 4.331410 & 0.519567 \\ Chroma & 9.998953 & 0.505080 \\ Mel-spectrogram & 0.604765 & 0.502307 \\ Zero crossing rate & 0.988847 & 0.606065 \\ Spectral centroid & 0.441515 & 0.534054 \\ Spectral bandwidth & 0.763678 & 0.547675 \\ Spectral contrast & 4.665370 & 0.498247 \\ Spectral rolloff & 0.539069 & 0.534808 \\ RMS energy & 0.259262 & 0.514598 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Optimal thresholds of KL divergence method for each feature after optimized on validation set with Bayesian optimization. Two naive systems considered as baselines for comparison, they are (i) naive\({}_{0}\): it is only output 0 (negative) label. (ii) naive\({}_{1}\): the system only output 1 (positive) label. Figure 3: Threshold optimization with Bayesian approach for KL divergence on validation set. Table 4 lists the FAR and MDR for the naive systems, which are shown here for comparison purposes with the KL-GMM and BIC-GMM. Tables A1-A10 lists the detailed results of two approaches across different features: Chroma, Mel-spectrogram, MFCC, Pitch, RMS energy, Spectral bandwidth, Spectral centroid, Spectral contrast, Spectral rolloff, and Zero crossing rate for different meeting records. First of all, compared to the naive systems, both KL-GMM and BIC-GMM that processing the audio features, doing a real detection of speaker changes. They reduce the errors found in naive systems by analyzing the audio, rather than relying on fixed assumptions. It can be seen from Figure 3d,e, although the threshold of KL-GMM with Mel-spectrogram (Table A2) feature and RMS energy feature (Table A5) was optimized for the validation set well, but on the test data, it produces extremely high FAR values (close to 1.0) but very low MDR values, indicating that it detects almost all change points, but at the cost of many false alarms. In this case, the performance of the KL-GMM is similar to the naive\({}_{1}\) system due to improper threshold values. KL-GMM with the pitch (Table A4) feature gives a high MDR value and a low FAR. In this situation, it behaves similarly to the naive\({}_{0}\) system, but slightly better. However, KL-GMM as a sensitive method to threshold, with feature MFCC (Table A3), and Zero cross rate feature (Table A10) shows a balanced result with FAR and MDR across different meeting test sets. This reflects the fact that optimizing a threshold value for KL divergence is challenging and may not always work well across different audio data.