51 citations · 52 across the 8 of their papers we have counts for
8 papers
Word meaning co-determines vowel-inherent spectral change. A corpus-based investigation of conversational Mandarin
Xiaoyun Jin, Mirjam Ernestus, R. Harald Baayen
This study investigates vowel-inherent spectral change (VISC) in spontaneous conversational Mandarin. Using the generalized additive model and word embeddings from distributional s…
Using embeddings to predict spoken word duration and pitch in Mandarin monosyllabic words
Xiaoyun Jin, Mirjam Ernestus, R. Harald Baayen
Time-normalized f0 contours of Mandarin words in conversational speech have been shown to be predictable in part from their contextualized embeddings (CEs). The present study inves…
A new kid on the block: Distributional semantics predicts the word-specific tone signatures of monosyllabic words in conversational Taiwan Mandarin
Xiaoyun Jin, Mirjam Ernestus, R. Harald Baayen
We present a corpus-based investigation of how the pitch contours of monosyllabic words are realized in spontaneous conversational Mandarin, focusing on the effects of words' meani…
A corpus-based investigation of pitch contours of monosyllabic words in conversational Taiwan Mandarin
Xiaoyun Jin, Mirjam Ernestus, R. Harald Baayen
In Mandarin, the tonal contours of monosyllabic words produced in isolation or in careful speech are characterized by four lexical tones: a high-level tone (T1), a rising tone (T2)…
Modelling word learning and recognition using visually grounded speech
Danny Merkx, Sebastiaan Scholten, Stefan L. Frank +2
Background: Computational models of speech recognition often assume that the set of target words is already given. This implies that these models do not learn to recognise speech f…
Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge
Danny Merkx, Stefan L. Frank, Mirjam Ernestus
Distributional semantic models capture word-level meaning that is useful in many natural language processing tasks and have even been shown to capture cognitive aspects of word mea…
Semantic sentence similarity: size does not always matter
Danny Merkx, Stefan L. Frank, Mirjam Ernestus
This study addresses the question whether visually grounded speech recognition (VGS) models learn to capture sentence semantics without access to any prior linguistic knowledge. We…
Language learning using Speech to Image retrieval
Danny Merkx, Stefan L. Frank, Mirjam Ernestus
Humans learn language by interaction with their environment and listening to other humans. It should also be possible for computational models to learn language directly from speec…