# Speaker Change Detection Using Features through A Neural Network Speaker Classifier

## TL;DR

This paper presents a neural network-based method for real-time speaker change detection that transforms speech features into likelihood vectors for accurate differentiation of speakers, achieving high accuracy and competitive performance.

## Contribution

The study introduces a novel neural network approach that converts conversational speech features into likelihood vectors, enabling effective speaker change detection with high accuracy.

## Key findings

- Achieved 100% speaker classification accuracy with at least 0.97 seconds of speech.
- Captured approximately 97% of speaker changes in short intervals.
- Performed competitively compared to other methods in literature.

## Abstract

The mechanism proposed here is for real-time speaker change detection in conversations, which firstly trains a neural network text-independent speaker classifier using in-domain speaker data. Through the network, features of conversational speech from out-of-domain speakers are then converted into likelihood vectors, i.e. similarity scores comparing to the in-domain speakers. These transformed features demonstrate very distinctive patterns, which facilitates differentiating speakers and enable speaker change detection with some straight-forward distance metrics. The speaker classifier and the speaker change detector are trained/tested using speech of the first 200 (in-domain) and the remaining 126 (out-of-domain) male speakers in TIMIT respectively. For the speaker classification, 100% accuracy at a 200 speaker size is achieved on any testing file, given the speech duration is at least 0.97 seconds. For the speaker change detection using speaker classification outputs, performance based on 0.5, 1, and 2 seconds of inspection intervals were evaluated in terms of error rate and F1 score, using synthesized data by concatenating speech from various speakers. It captures close to 97% of the changes by comparing the current second of speech with the previous second, which is very competitive among literature using other methods.