← Search

Marvin Lavechin

2 accepted papers

2025

From perception to production: how acoustic invariance facilitates articulatory learning in a self-supervised vocal imitation model

EMNLP 2025

Human infants face a formidable challenge in speech acquisition: mapping extremely variable acoustic inputs into appropriate articulatory movements without explicit instruction. We present a computational model that addresses the acoustic-to-articulatory mapping problem through self-supervised learn

Cited by 0SourcePDFScholar
2020

Pyannote.Audio: Neural Building Blocks for Speaker Diarization

ICASSP 2020accepted

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-end neural building blocks that can be combined and jointly optimized to build speaker diarization pipelines. pyannote.aud…

Cited by 476SourceScholar