A review of speaker diarization: Recent advances with deep learning
Speaker diarization is a task to label audio or video recordings with classes that correspond
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …
Bigssl: Exploring the frontier of large-scale semi-supervised learning for automatic speech recognition
We summarize the results of a host of efforts using giant automatic speech recognition (ASR)
models pre-trained using large, diverse unlabeled datasets containing approximately a …
models pre-trained using large, diverse unlabeled datasets containing approximately a …
CHiME-6 challenge: Tackling multispeaker speech recognition for unsegmented recordings
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 …
6th CHiME Speech Separation and Recognition Challenge (CHiME-6). The new challenge …
Speechstew: Simply mix all available speech recognition data to train one large neural network
We present SpeechStew, a speech recognition model that is trained on a combination of
various publicly available speech recognition datasets: AMI, Broadcast News, Common …
various publicly available speech recognition datasets: AMI, Broadcast News, Common …
End-to-end neural speaker diarization with self-attention
Speaker diarization has been mainly developed based on the clustering of speaker
embeddings. However, the clustering-based approach has two major problems; ie,(i) it is not …
embeddings. However, the clustering-based approach has two major problems; ie,(i) it is not …
End-to-end neural speaker diarization with permutation-free objectives
In this paper, we propose a novel end-to-end neural-network-based speaker diarization
method. Unlike most existing methods, our proposed method does not have separate …
method. Unlike most existing methods, our proposed method does not have separate …
The chime-7 dasr challenge: Distant meeting transcription with multiple devices in diverse scenarios
The CHiME challenges have played a significant role in the development and evaluation of
robust automatic speech recognition (ASR) systems. We introduce the CHiME-7 distant ASR …
robust automatic speech recognition (ASR) systems. We introduce the CHiME-7 distant ASR …
Far-field automatic speech recognition
The machine recognition of speech spoken at a distance from the microphones, known as
far-field automatic speech recognition (ASR), has received a significant increase in attention …
far-field automatic speech recognition (ASR), has received a significant increase in attention …
Rethinking evaluation in asr: Are our models robust enough?
Is pushing numbers on a single benchmark valuable in automatic speech recognition?
Research results in acoustic modeling are typically evaluated based on performance on a …
Research results in acoustic modeling are typically evaluated based on performance on a …
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis
Multi-speaker speech recognition of unsegmented recordings has diverse applications such
as meeting transcription and automatic subtitle generation. With technical advances in …
as meeting transcription and automatic subtitle generation. With technical advances in …