From 48ee4dd536242f25bb157c7962941c3661b4286b Mon Sep 17 00:00:00 2001 From: yhliang <429259365@qq.com> Date: 星期三, 10 五月 2023 17:12:30 +0800 Subject: [PATCH] fix format error --- docs/m2met2/_build/html/_sources/Introduction.md.txt | 2 +- 1 files changed, 1 insertions(+), 1 deletions(-) diff --git a/docs/m2met2/_build/html/_sources/Introduction.md.txt b/docs/m2met2/_build/html/_sources/Introduction.md.txt index 7107bf5..06c27b2 100644 --- a/docs/m2met2/_build/html/_sources/Introduction.md.txt +++ b/docs/m2met2/_build/html/_sources/Introduction.md.txt @@ -6,7 +6,7 @@ The ICASSP2022 M2MeT challenge focuses on meeting scenarios, and it comprises two main tasks: speaker diarization and multi-speaker automatic speech recognition. The former involves identifying who spoke when in the meeting, while the latter aims to transcribe speech from multiple speakers simultaneously, which poses significant technical difficulties due to overlapping speech and acoustic interferences. -Building on the success of the previous M2MeT challenge, we are excited to propose the M2MeT2.0 challenge as an ASRU2023 challenge special session. In the original M2MeT challenge, the evaluation metric was speaker-independent, which meant that the transcription could be determined, but not the corresponding speaker. To address this limitation and further advance the current multi-talker ASR system towards practicality, the M2MeT2.0 challenge proposes the speaker-attributed ASR task with two sub-tracks: fixed and open training conditions. The speaker-attribute automatic speech recognition (ASR) task aims to tackle the practical and challenging problem of identifying "who spoke what at when". To facilitate reproducible research in this field, we offer a comprehensive overview of the dataset, rules, evaluation metrics, and baseline systems. Furthermore, we will release a carefully curated test set, comprising approximately 10 hours of audio, according to the timeline. The new test set is designed to enable researchers to validate and compare their models' performance and advance the state of the art in this area. +Building on the success of the previous M2MeT challenge, we are excited to propose the M2MeT2.0 challenge as an ASRU 2023 challenge special session. In the original M2MeT challenge, the evaluation metric was speaker-independent, which meant that the transcription could be determined, but not the corresponding speaker. To address this limitation and further advance the current multi-talker ASR system towards practicality, the M2MeT2.0 challenge proposes the speaker-attributed ASR task with two sub-tracks: fixed and open training conditions. The speaker-attribute automatic speech recognition (ASR) task aims to tackle the practical and challenging problem of identifying "who spoke what at when". To facilitate reproducible research in this field, we offer a comprehensive overview of the dataset, rules, evaluation metrics, and baseline systems. Furthermore, we will release a carefully curated test set, comprising approximately 10 hours of audio, according to the timeline. The new test set is designed to enable researchers to validate and compare their models' performance and advance the state of the art in this area. ## Timeline(AOE Time) - $ April~29, 2023: $ Challenge and registration open. -- Gitblit v1.9.1