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Found 12 papers, 10 papers with code
Analyzing BERT Cross-lingual Transfer Capabilities in Continual Sequence Labeling
Knowledge transfer between neural language models is a widely used technique that has proven to improve performance in a multitude of natural language tasks, in particular with the recent rise of large pre-trained language models like BERT.
Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation
Most automatic speech processing systems are sensitive to the acoustic environment, with degraded performance when applied to noisy or reverberant speech.
Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation
We propose to address online speaker diarization as a combination of incremental clustering and local diarization applied to a rolling buffer updated every 500ms.
End-to-end speaker segmentation for overlap-aware resegmentation
Experiments on multiple speaker diarization datasets conclude that our model can be used with great success on both voice activity detection and overlapped speech detection.
An open-source voice type classifier for child-centered daylong recordings
Spontaneous conversations in real-world settings such as those found in child-centered recordings have been shown to be amongst the most challenging audio files to process.
A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification
Despite the growing popularity of metric learning approaches, very little work has attempted to perform a fair comparison of these techniques for speaker verification.
The Speed Submission to DIHARD II: Contributions & Lessons Learned
This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team.
pyannote.audio: neural building blocks for speaker diarization
We introduce pyannote. audio, an open-source toolkit written in Python for speaker diarization.
Ranked #1 on
Speaker Diarization
on ETAPE
End-to-end Domain-Adversarial Voice Activity Detection
In the in-domain scenario where the training and test sets cover the exact same domains, we show that the domain-adversarial approach does not degrade performance of the proposed end-to-end model.
Audio and Speech Processing I.2.7
LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization
More and more neural network approaches have achieved considerable improvement upon submodules of speaker diarization system, including speaker change detection and segment-wise speaker embedding extraction.
TristouNet: Triplet Loss for Speaker Turn Embedding
TristouNet is a neural network architecture based on Long Short-Term Memory recurrent networks, meant to project speech sequences into a fixed-dimensional euclidean space.
