60 citations · 108 across the 14 of their papers we have counts for
12 papers · 1 filter
Deriving Benchmarking Datasets from Long-Form Recordings: Challenges and Opportunities
Kaveri K. Sheth, Lawrence Borst, Tarek Kunze +6
Long-form recordings (LFRs) of child-centered audio are ecologically valid sources for studying early language development, but three problems limit their use. First, LFR corpora a…
Context-aware child-directed speech detection from long-form recordings
Théo Charlot, Tarek Kunze, Kaveri K. Sheth +2
Automatically distinguishing child-directed speech from adult-directed speech in long-form recordings is key to scalable analyses of children's language environments. Existing appr…
Automated Analysis of Naturalistic Recordings in Early Childhood: Applications, Challenges, and Opportunities
Jialu Li, Marvin Lavechin, Xulin Fan +4
Naturalistic recordings capture audio in real-world environments where participants behave naturally without interference from researchers or experimental protocols. Naturalistic l…
BabyHuBERT: Multilingual Self-Supervised Learning for Segmenting Speakers in Child-Centered Long-Form Recordings
Théo Charlot, Tarek Kunze, Maxime Poli +3
Child-centered daylong recordings are essential for studying early language development, but existing speech models trained on clean adult data perform poorly due to acoustic and l…
Fifteen Years of Child-Centered Long-Form Recordings: Promises, Resources, and Remaining Challenges to Validity
Loann Peurey, Marvin Lavechin, Tarek Kunze +4
Audio-recordings collected with a child-worn device are a fundamental tool in child language research. Long-form recordings collected over whole days promise to capture children's…
Challenges in Automated Processing of Speech from Child Wearables: The Case of Voice Type Classifier
Tarek Kunze, Marianne Métais, Hadrien Titeux +5
Recordings gathered with child-worn devices promised to revolutionize both fundamental and applied speech sciences by allowing the effortless capture of children's naturalistic spe…
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…
Probing phoneme, language and speaker information in unsupervised speech representations
Maureen de Seyssel, Marvin Lavechin, Yossi Adi +2
Unsupervised models of representations based on Contrastive Predictive Coding (CPC)[1] are primarily used in spoken language modelling in that they encode phonetic information. In…
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…
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…
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…
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…