ADVOSYNTH: A Synthetic Multi-Advocate Dataset for Speaker Identification in Courtroom Scenarios
This addresses the need for specialized datasets to study speaker identification in synthetic courtroom scenarios, but it is incremental as it focuses on a new dataset rather than a novel method.
The paper tackles the problem of distinguishing synthetic voices in structured environments by introducing Advosynth-500, a dataset of 100 synthetic speech files with 10 unique advocate identities, and presents a speaker identification challenge to evaluate modern systems.
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.