AliMeeting
Emerging20papers using it
2021first seen
The 'AliMeeting' dataset is a large-scale conversational dataset used to evaluate end-to-end speaker diarization and recognition systems.
Papers using AliMeeting (20)
- SoulX-Transcriber: A Robust End-to-End Framework for Multi-Speaker Speech TranscriptionBalancing ASR and diarization in end-to-end LLMs for multi-talker speech recognitionSpeaker-Reasoner: Scaling Interaction Turns and Reasoning Patterns for Timestamped Speaker-Attributed ASRJoint Learning Global-Local Speaker Classification to Enhance End-to-End Speaker Diarization and RecognitionSpatialEmb: Extract and Encode Spatial Information for 1-Stage Multi-channel Multi-speaker ASR on Arbitrary Microphone ArraysLightweight and Robust Multi-Channel End-to-End Speech Recognition with Spherical Harmonic TransformM2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription
ChallengeGPU-accelerated Guided Source Separation for Meeting TranscriptionRoyalflush Speaker Diarization System for ICASSP 2022 Multi-channel
Multi-party Meeting Transcription ChallengeConcurrent Speaker Detection: A multi-microphone Transformer-Based
ApproachSimultaneous Speech Extraction for Multiple Target Speakers under the
Meeting ScenariosAdapting Multi-Lingual ASR Models for Handling Multiple TalkersDiariST: Streaming Speech Translation with Speaker DiarizationCross-Channel Attention-Based Target Speaker Voice Activity Detection:
Experimental Results for M2MeT ChallengeThe USTC-Ximalaya system for the ICASSP 2022 multi-channel multi-party
meeting transcription (M2MeT) challengeA Comparative Study on Speaker-attributed Automatic Speech Recognition
in Multi-party MeetingsOnline Target Speaker Voice Activity Detection for Speaker DiarizationSA-Paraformer: Non-autoregressive End-to-End Speaker-Attributed ASREnd-to-end Online Speaker Diarization with Target Speaker TrackingMulti-Channel Multi-Speaker ASR Using Target Speaker's Solo Segment