5 citations · 9 across the 5 of their papers we have counts for
10 papers
A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery
Bolaji Yusuf, Lucas Ondel, Lukas Burget +2
In this work, we propose a hierarchical subspace model for acoustic unit discovery. In this approach, we frame the task as one of learning embeddings on a low-dimensional phonetic…
The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units
Ewan Dunbar, Julien Karadayi, Mathieu Bernard +6
We present the Zero Resource Speech Challenge 2020, which aims at learning speech representations from raw audio signals without any labels. It combines the data sets and metrics f…
Bayesian Subspace HMM for the Zerospeech 2020 Challenge
Bolaji Yusuf, Lucas Ondel
In this paper we describe our submission to the Zerospeech 2020 challenge, where the participants are required to discover latent representations from unannotated speech, and to us…
The Zero Resource Speech Challenge 2019: TTS without T
Ewan Dunbar, Robin Algayres, Julien Karadayi +10
We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without t…
Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery
Lucas Ondel, Hari Krishna Vydana, Lukáš Burget +1
This work tackles the problem of learning a set of language specific acoustic units from unlabeled speech recordings given a set of labeled recordings from other languages. Our app…
Unsupervised Word Segmentation from Speech with Attention
Pierre Godard, Marcely Zanon-Boito, Lucas Ondel +4
We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource,…