13 citations · 23 across the 6 of their papers we have counts for
6 papers
Text-based Audio Retrieval by Learning from Similarities between Audio Captions
Huang Xie, Khazar Khorrami, Okko Räsänen +1
This paper proposes to use similarities of audio captions for estimating audio-caption relevances to be used for training text-based audio retrieval systems. Current audio-caption…
Integrating Continuous and Binary Relevances in Audio-Text Relevance Learning
Huang Xie, Khazar Khorrami, Okko Räsänen +1
Audio-text relevance learning refers to learning the shared semantic properties of audio samples and textual descriptions. The standard approach uses binary relevances derived from…
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…
Analysing the Impact of Audio Quality on the Use of Naturalistic Long-Form Recordings for Infant-Directed Speech Research
María Andrea Cruz Blandón, Alejandrina Cristia, Okko Räsänen
Modelling of early language acquisition aims to understand how infants bootstrap their language skills. The modelling encompasses properties of the input data used for training the…
Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model
Puyuan Peng, Shang-Wen Li, Okko Räsänen +2
In this paper, we show that representations capturing syllabic units emerge when training a self-supervised speech model with a visually-grounded training objective. We demonstrate…
ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 edition
Afra Alishahi, Grzegorz Chrupała, Alejandrina Cristia +5
We present the visually-grounded language modelling track that was introduced in the Zero-Resource Speech challenge, 2021 edition, 2nd round. We motivate the new track and discuss…