ADVOSYNTH: A Synthetic Multi-Advocate Dataset for Speaker Identification in Courtroom Scenarios

TL;DR

This paper presents ADVOSYNTH-500, a synthetic dataset of courtroom advocate voices designed to evaluate speaker identification systems in structured, high-fidelity speech environments.

Contribution

It introduces a novel synthetic dataset with detailed advocate voice characteristics for benchmarking speaker identification in courtroom scenarios.

Findings

Dataset includes 100 synthetic speech files with 10 advocate identities.

Simulates courtroom dialogues with five advocate pairs.

Provides a new benchmark for speaker identification in synthetic, structured environments.

Abstract

As large-scale speech-to-speech models achieve high fidelity, the distinction between synthetic voices in structured environments becomes a vital area of study. This paper introduces Advosynth-500, a specialized dataset comprising 100 synthetic speech files featuring 10 unique advocate identities. Using the Speech Llama Omni model, we simulate five distinct advocate pairs engaged in courtroom arguments. We define specific vocal characteristics for each advocate and present a speaker identification challenge to evaluate the ability of modern systems to map audio files to their respective synthetic origins. Dataset is available at this link-https: //github.com/naturenurtureelite/ADVOSYNTH-500.