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Found 15 papers, 12 papers with code
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.
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.
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
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
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.
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.
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.
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.
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.
Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation
Most automatic speech processing systems register degraded performance when applied to noisy or reverberant speech.
BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models
Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels.
Powerset multi-class cross entropy loss for neural speaker diarization
Since its introduction in 2019, the whole end-to-end neural diarization (EEND) line of work has been addressing speaker diarization as a frame-wise multi-label classification problem with permutation-invariant training.
PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker Recordings
A major drawback of supervised speech separation (SSep) systems is their reliance on synthetic data, leading to poor real-world generalization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
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.
