arXiv:2510.23320v2 Announce Type: replace-cross Abstract: We introduce LibriConvo, a synthetic conversational speech corpus for speaker diarization and automatic speech recognition (ASR), built by instantiating the previously proposed Speaker-Aware Simulated Conversation (SASC) framework in a dataset and benchmarking setting. The main contribution of this paper is a corpus construction pipeline and benchmark derived from that framework. To make the data more suitable for downstream ASR and diarization, conversational timing statistics are estimated from English CallHome using external voice ac
The continuous demand for more robust and diverse training data for advanced AI models drives the development of synthetic datasets like LibriConvo.
Improved conversational speech datasets are critical for advancing Automatic Speech Recognition (ASR) and speaker diarization, which are foundational technologies for many AI applications.
The availability of large-scale, high-quality synthetic conversational speech data reduces reliance on real-world recordings, enabling faster iteration and more specialized model training.
- · AI/ML researchers
- · Speech technology companies
- · Developers of conversational AI
- · Speech data collection services
ASR and diarization models become more accurate and robust in complex conversational environments.
This improvement facilitates the deployment of more sophisticated voice user interfaces and AI agents capable of understanding multi-speaker interactions.
Enhanced conversational AI leads to new applications in customer service, accessibility, and human-computer interaction, potentially impacting white-collar workflows.
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