18 citations · 47 across the 13 of their papers we have counts for
13 papers
Optimal Resource Scheduling and Allocation in Distributed Computing Systems
Wei Ren, Eleftherios Vlahakis, Nikolaos Athanasopoulos +1
The essence of distributed computing systems is how to schedule incoming requests and how to allocate all computing nodes to minimize both time and computation costs. In this paper…
Revisiting joint decoding based multi-talker speech recognition with DNN acoustic model
Martin Kocour, Kateřina Žmolíková, Lucas Ondel +5
In typical multi-talker speech recognition systems, a neural network-based acoustic model predicts senone state posteriors for each speaker. These are later used by a single-talker…
Unsupervised Word Segmentation from Discrete Speech Units in Low-Resource Settings
Marcely Zanon Boito, Bolaji Yusuf, Lucas Ondel +2
Documenting languages helps to prevent the extinction of endangered dialects, many of which are otherwise expected to disappear by the end of the century. When documenting oral lan…
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,…
Bayesian Models for Unit Discovery on a Very Low Resource Language
Lucas Ondel, Pierre Godard, Laurent Besacier +7
Developing speech technologies for low-resource languages has become a very active research field over the last decade. Among others, Bayesian models have shown some promising resu…
Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop
Odette Scharenborg, Laurent Besacier, Alan Black +16
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and word…
Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages
Matthew Wiesner, Chunxi Liu, Lucas Ondel +6
Automatic speech recognition (ASR) systems often need to be developed for extremely low-resource languages to serve end-uses such as audio content categorization and search. While…
An Empirical Evaluation of Zero Resource Acoustic Unit Discovery
Chunxi Liu, Jinyi Yang, Ming Sun +7
Acoustic unit discovery (AUD) is a process of automatically identifying a categorical acoustic unit inventory from speech and producing corresponding acoustic unit tokenizations. A…