115 citations · 383 across the 11 of their papers we have counts for
14 papers
Powerset multi-class cross entropy loss for neural speaker diarization
Alexis Plaquet, Hervé Bredin
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 probl…
BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models
Marvin Lavechin, Yaya Sy, Hadrien Titeux +5
Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels. In order…
Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation
Marvin Lavechin, Marianne Métais, Hadrien Titeux +7
Most automatic speech processing systems register degraded performance when applied to noisy or reverberant speech. But how can one tell whether speech is noisy or reverberant? We…
Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation
Juan M. Coria, Hervé Bredin, Sahar Ghannay +1
We propose to address online speaker diarization as a combination of incremental clustering and local diarization applied to a rolling buffer updated every 500ms. Every single step…
End-to-end speaker segmentation for overlap-aware resegmentation
Hervé Bredin, Antoine Laurent
Speaker segmentation consists in partitioning a conversation between one or more speakers into speaker turns. Usually addressed as the late combination of three sub-tasks (voice ac…
An open-source voice type classifier for child-centered daylong recordings
Marvin Lavechin, Ruben Bousbib, Hervé Bredin +2
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. Neverth…
A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification
Juan M. Coria, Hervé Bredin, Sahar Ghannay +1
Despite the growing popularity of metric learning approaches, very little work has attempted to perform a fair comparison of these techniques for speaker verification. We try to fi…
Speaker detection in the wild: Lessons learned from JSALT 2019
Paola Garcia, Jesus Villalba, Herve Bredin +21
This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios. The main focus was to tack…
The Speed Submission to DIHARD II: Contributions & Lessons Learned
Md Sahidullah, Jose Patino, Samuele Cornell +11
This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team. Besides describing the system, whic…
pyannote.audio: neural building blocks for speaker diarization
Hervé Bredin, Ruiqing Yin, Juan Manuel Coria +7
We introduce pyannote.audio, an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-en…
Overlap-aware diarization: resegmentation using neural end-to-end overlapped speech detection
Latané Bullock, Hervé Bredin, Leibny Paola Garcia-Perera
We address the problem of effectively handling overlapping speech in a diarization system. First, we detail a neural Long Short-Term Memory-based architecture for overlap detection…
End-to-end Domain-Adversarial Voice Activity Detection
Marvin Lavechin, Marie-Philippe Gill, Ruben Bousbib +2
Voice activity detection is the task of detecting speech regions in a given audio stream or recording. First, we design a neural network combining trainable filters and recurrent l…
LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization
Qingjian Lin, Ruiqing Yin, Ming Li +2
More and more neural network approaches have achieved considerable improvement upon submodules of speaker diarization system, including speaker change detection and segment-wise sp…
TristouNet: Triplet Loss for Speaker Turn Embedding
Hervé Bredin
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. Thanks t…