# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 5 Conclusions  
In this paper, an empirical comparative study using various audio features with different methods for unsupervised SCD is presented. A various of audio features were evaluated for two methods: BIC-GMM and KL divergence with GMM, the former approach is no need threshold optimization, but the latter does. Various features were experimented such as MFCC, mel-spectrogram, spectral centroid, spectral bandwidth, spectral contrast, spectral rolloff, RMS, pitch, zero crossing rate. In order to gain insight into which features have a significant impact on the SCD problem, two conditional distributions from a corpus, such as the probability of feature change given a speaker change \(P(FC|SC)\) and the probability of speaker change given feature change were calculated. Crossing points between these two probabilities and the maximum \(P(SC|FC)\) for each features were calculated. The crossing points represent the thresholds at which sensitivity and specificity are balanced, marking where the two probabilities are equal. The maximum \(P(SC|FC)\) giving insights how closely feature changes align with speaker changes. Different experiments were carried out for comparing the combinations of method across features: (i) the threshold was optimized on the validation set for KL-GMM approach using Bayesian optimization process. (ii) results for two naive systems were reported as baseline considering the situation of unbalanced class of SCD. (iii) evaluate each combination of audio feature with different methods on the test data, and report the evaluation results. (iv) averaging the results for each method across all features to identify the most suitable features to SCD. For evaluation, false alarm rate and missed detection rate were calculated. Experimental results can be summarized as follows:  
1. For different features, KL-GMM approache using Bayesian optimization process to find optimal threshold value on the validation set and the optimal value with the optimization process was reported. 2. Comparing with two naive systems, both KL-GMM and BIC-GMM that processing the audio features, doing a real detection of speaker changes. 3. KL-GMM peformance with some feature combination similar to two naive systems, like giving extreme high value to FAR or low value to MDR, vice versa. Although, the thresholds are well optimized on the validation set, it still fails to handle the variability of new audio dataset. KL-GMM as a sensitive mehtod to threshold, but with features like MFCC and zero cross rate shows a better performance. BIC-GMM showed a robust performance on handling the variability of different feature  
Figure 5: Relation between Max\(P(SC|FC)\) vs. Approx. F1 score (the higher value is good). values compared it with KL-GMM, and it achieves more stable performance across different meetings. * Average results indicate that both BIC-GMM and KL-GMM perform effectively on the SCD when using MFCC features. This suggests that MFCC features are well-suited for SCD with these techniques. With the highest \(P(SC|FC)\) value, MFCC, also has the lowest MDR and FAR, confirming the expectations derived from the statistics. * The relation between Max\(P(SC|FC)\) vs approx. F1 score showed that MFCC consistently ranks high across both methods, confirming its effectiveness. Zero crossing rate, Chroma and Spectral contrast gives a higher performance in BIC-GMM. Mel-spectrogram consistently ranks at the bottom, suggesting it has the least impact on performance across both methods. The potential future work can be summarized as follows: (i) applying deep learning methods and comparing their results with those obtained using traditional MFCC features; (ii) exploring the use of pre-trained large audio models for the SCD task; (iii) investigating a multi-modal SCD approach that combines audio signal data with its transcription, leveraging both large audio models and large language models  
Conceptualization, A.T. and G.T. ; methodology, A.T. and G.T. ; software, A.T. and G.T. ; validation, A.T. and G.T. ; formal analysis, A.T. and G.T.