Aishell-4

AISHELL-4 is a dataset used to evaluate speaker diarization and recognition systems, containing conversational audio data that facilitates the assessment of speaker classification performance.

Papers using Aishell-4 (9)

SoulX-Transcriber: A Robust End-to-End Framework for Multi-Speaker Speech Transcription2026Balancing ASR and diarization in end-to-end LLMs for multi-talker speech recognition2026Speaker-Reasoner: Scaling Interaction Turns and Reasoning Patterns for Timestamped Speaker-Attributed ASR2026Joint Learning Global-Local Speaker Classification to Enhance End-to-End Speaker Diarization and Recognition2026Lightweight and Robust Multi-Channel End-to-End Speech Recognition with Spherical Harmonic Transform2025Exploiting Single-Channel Speech For Multi-channel End-to-end Speech Recognition2021Exploiting Single-Channel Speech for Multi-Channel End-to-End Speech Recognition: A Comparative Study2022Token-level Speaker Change Detection Using Speaker Difference and Speech Content via Continuous Integrate-and-fire2022ASoBO: Attentive Beamformer Selection for Distant Speaker Diarization in Meetings2024