A Framework For Synthetic Audio Conversations Generation Using Large Language Models

Abstract

In this paper, we introduce ConversaSynth, a framework designed to generate synthetic conversation audio using large language models (LLMs) with multiple persona settings. The framework first creates diverse and coherent text-based dialogues across various topics, which are then converted into audio using text-to-speech (TTS) systems. Our experiments demonstrate that ConversaSynth effectively generates highquality synthetic audio datasets, which can significantly enhance the training and evaluation of models for audio tagging, audio classification, and multi-speaker speech recognition. The results indicate that the synthetic datasets generated by ConversaSynth exhibit substantial diversity and realism, making them suitable for developing robust, adaptable audio-based AI systems.

Stats

• citations2
• S2 citations—
• github stars0
• HF likes0
• heat score3.58
• arxiv keykyaw2024a

Related papers

• Generating Data With Text-to-speech And Large-language Models For Conversational Speech Recognition (2024)6.34
• Audiolm: A Language Modeling Approach To Audio Generation (2022)18.91
• From Alignment To Advancement: Bootstrapping Audio-language Alignment With Synthetic Data (2025)2.26
• Enhancing Code-switched Text-to-speech Synthesis Capability In Large Language Models With Only Monolingual Corpora (2024)0.00
• Prompting Large Language Models With Audio For General-purpose Speech Summarization (2024)6.34
• Paralinguistics-enhanced Large Language Modeling Of Spoken Dialogue (2023)0.00
• Style-talker: Finetuning Audio Language Model And Style-based Text-to-speech Model For Fast Spoken Dialogue Generation (2024)0.00
• Paralinguistics-aware Speech-empowered Large Language Models For Natural Conversation (2024)3.96