LibriConvo: New Synthetic Conversational Speech Dataset for Speech Recognition and Speaker Identification
Researchers have released LibriConvo, a 240-hour synthetic conversational speech corpus designed to benchmark automatic speech recognition (ASR) and speaker diarization systems. Built on the Speaker-Aware Simulated Conversation framework, it uses LibriTTS audio grouped by book and calibrated with real conversational timing statistics from English CallHome. The corpus provides a reproducible, speaker-disjoint benchmark at a scale and realism not previously available for synthetic conversational speech evaluation.
LibriConvo is a newly introduced synthetic conversational speech dataset containing 240.1 hours of audio across 1,496 dialogues involving 830 speakers, partitioned into speaker-disjoint train, validation, and test splits. The corpus is constructed by instantiating the Speaker-Aware Simulated Conversation (SASC) framework, with several enhancements to improve realism: conversational timing statistics are estimated from English CallHome, long pauses are compressed, LibriTTS utterances are grouped by book for local semantic coherence, and room impulse responses are selected using a spatial-plausibility heuristic. Baseline diarization results show Sortformer substantially outperforming the pyannote pipeline on the test split, achieving an 11.1% Diarization Error Rate (DER) versus 24.4%. For ASR, a Fast Conformer-CTC XLarge model fine-tuned with Serialized Output Training reached 7.29% Word Error Rate (WER) and 6.97% concatenated-minimum-permutation WER (cpWER), surpassing zero-shot Whisper-large-v3. The work has been accepted at TSD 2026 and positions LibriConvo as a practical, openly available benchmark for multi-speaker speech processing research.
What's missing
The paper does not report how LibriConvo baselines compare to models trained or evaluated on real conversational corpora (e.g., AMI, CALLHOME), leaving the domain gap between synthetic and natural conversation unquantified. It is also unclear whether the corpus and pipeline code are publicly released or under what license, which affects reproducibility.
What different sources said
- arXiv cs.CLCenter
LibriConvo: Simulating Conversations from Read Literature for ASR and Diarization
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