# Comparative Analysis of Audio Features for Unsupervised Speaker Change Detection
## 1 Introduction  
Speaker change detection (SCD) is an important task in speech processing, vital for applications like speaker diarization [1, 2], automated transcription [3], and audio indexing [4]. The goal of speaker change detection is to identify timestamps in an audio stream where a speaker's segment ends and a new one begins, enabling accurate segmentation of speech data. With the rapid growth of audio data in various domains, from meetings and lectures to podcasts and media archives, the demand for accurate and efficient speaker change point detection has significantly increased. This surge in data volume necessitates robust methods capable of handling diverse and challenging scenarios. A fast and simple approach to SCD is therefore essential to process this vast array of audio efficiently, ensuring that these applications can operate effectively even in the face of increasing data complexity. Given the complex nature of audio signals, which often include varying pitch, tone, and amplitude, the task becomes even more challenging when multiple voices overlap, background noise is present. Speaker change detection has been studied for more than a decade, and unsupervised methods for detecting speaker change points using metrics have become representative. In these methods, speaker change points are detected by comparing features from two consecutive audio frames. For instance, in a approach of Bayesian information criterion (BIC) [5], audio features are extracted from audio frames, then the distribution of the speechfeature is assumed to follow mixture Gaussian distribution. The speaker change points are identified by evaluating two hypotheses from these distribution of consecutive frames: (i) the first hypothesis assumes that the left and right segments of speech originate from the same speaker, (ii) while the second hypothesis posits that the two segments come from different speakers. Numerous criteria for the metric-based approach have been proposed to assess the likelihood of the two hypotheses, such as the Kullback Leibler (KL) distance [6], generalized likelihood ratio (GLR) [7; 8]. Among these criteria, KL has been the most widely used method, along with its numerous variants [9; 10]. Recent developments in audio feature extraction, such as i-vector [11] and deep learning-based approaches [12; 13], have changed the SCD method to uniform segmentation, as varying segment lengths introduced additional variability into speaker representation and deteriorated the fidelity of the speaker representations. Despite advancements in feature extraction of audio data and modeling techniques for SCD, there's still a gap in understanding how various audio features influence different methods in SCD, particularly in unsupervised settings. Audio features such as mel-frequency cepstral coefficients (MFCC), pitch, and mel-spectrogram, etc., and they contain different aspects of speech, but their effectiveness in identifying speaker changes is not clearly studied. Without a thorough comparative analysis, it's challenging to pinpoint which features are most effective for unsupervised SCD. In this paper, an empirical comparative study using various audio features with different methods for unsupervised SCD is presented. The goal of study is to analyze how various audio features influence the different method in SCD. In order to conduct an comparative analysis, various features were analyzed such as MFCC, mel-spectrogram, spectral centroid, spectral bandwidth, spectral contrast, spectral rolloff, RMS, pitch, zero crossing rate. These 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.