LibriConvo: New Synthetic Conversational Speech Dataset for Speech Recognition and Speaker Identification
Researchers introduced LibriConvo, a synthetic conversational speech corpus containing 240.1 hours of audio designed for automatic speech recognition (ASR) and speaker diarization tasks. The dataset was created using the Speaker-Aware Simulated Conversation framework, with audio sourced from LibriTTS and timing statistics from CallHome recordings. The benchmark demonstrates that specialized models outperform general-purpose systems like Whisper on these tasks, establishing LibriConvo as a practical resource for developing multi-speaker speech processing systems.
LibriConvo is a newly constructed synthetic conversational speech dataset comprising 240.1 hours of audio across 1,496 dialogues involving 830 speakers, designed to advance research in automatic speech recognition and speaker diarization. The corpus was built using the Speaker-Aware Simulated Conversation (SASC) framework with several refinements: conversational timing statistics estimated from English CallHome data, compressed long pauses, LibriTTS utterances grouped by book for semantic continuity, and room impulse responses selected using spatial-plausibility heuristics. Baseline evaluations show that a Sortformer model achieves 11.1% diarization error rate compared to 24.4% for the pyannote pipeline, while a Fast Conformer-CTC XLarge model fine-tuned with Serialized Output Training achieves 7.29% word error rate for ASR, outperforming zero-shot Whisper-large-v3. The dataset is partitioned into speaker-disjoint train, validation, and test splits to ensure rigorous evaluation. These results position LibriConvo as a practical benchmark for studying synthetic conversational speech and evaluating multi-speaker speech processing systems.
What's missing
The paper does not discuss potential limitations of synthetic data generalization to real-world conversational speech, nor does it address how well models trained on LibriConvo transfer to other languages or acoustic environments beyond the English CallHome baseline.
What different sources said
- arXiv cs.CLCenter
LibriConvo: Simulating Conversations from Read Literature for ASR and Diarization
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