Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges we
organize the 6th CHiME Speech Separation and Recognition Challenge (CHiME-6).
The new challenge revisits the previous CHiME-5 challenge and further considers
the problem of distant multi-microphone conversational speech diarization and
recognition in everyday home environments. Speech material is the same as the
previous CHiME-5 recordings except for accurate array synchronization. The
material was elicited using a dinner party scenario with efforts taken to
capture data that is representative of natural conversational speech. This
paper provides a baseline description of the CHiME-6 challenge for both
segmented multispeaker speech recognition (Track 1) and unsegmented
multispeaker speech recognition (Track 2). Of note, Track 2 is the first
challenge activity in the community to tackle an unsegmented multispeaker
speech recognition scenario with a complete set of reproducible open source
baselines providing speech enhancement, speaker diarization, and speech
recognition modules.

本文介绍了基于 CHiME-6 Speech Separation and Recognition Challenge（CHiME-6）的多说话人语音分离和识别问题，该比赛首次尝试了基于无分割的多说话人语音识别方案，并提供了完整的可重现性开源基线方案。

CHiME-6 Challenge：处理未分割录音的多说话人语音识别

CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for  Unsegmented Recordings

We present a multispeaker, multilingual text-to-speech (TTS) synthesis model
based on Tacotron that is able to produce high quality speech in multiple
languages. Moreover, the model is able to transfer voices across languages,
e.g. synthesize fluent Spanish speech using an English speaker's voice, without
training on any bilingual or parallel examples. Such transfer works across
distantly related languages, e.g. English and Mandarin.
Critical to achieving this result are: 1. using a phonemic input
representation to encourage sharing of model capacity across languages, and 2.
incorporating an adversarial loss term to encourage the model to disentangle
its representation of speaker identity (which is perfectly correlated with
language in the training data) from the speech content. Further scaling up the
model by training on multiple speakers of each language, and incorporating an
autoencoding input to help stabilize attention during training, results in a
model which can be used to consistently synthesize intelligible speech for
training speakers in all languages seen during training, and in native or
foreign accents.

该论文基于 Tacotron 合成模型，采用多说话人、多语言合成的方式，在没有任何双语或平行示例的情况下，实现了跨语言的语音转移。通过使用音素输入表征，鼓励模型跨语言共享模型容量，并结合对抗损失项，鼓励模型将语音内容与说话人身份表示分离，可以训练出一个可以用于所有训练语言的说话人的稳定可靠的语音合成模型。