20 citations · 74 across the 21 of their papers we have counts for
7 papers · 1 filter
Looking for Affect in Spontaneous Finnish Speech through Linguistic Interpretability
Kalle Lahtinen, Liisa Mustanoja, Okko Räsänen
Existing research on affect in speech has shown how acoustic surface characteristics and content-related linguistic aspects of speech both relate to perceived emotional arousal and…
Age-Dependent Analysis and Stochastic Generation of Child-Directed Speech
Okko Räsänen, Daniil Kocharov
Child-directed speech (CDS) is a particular type of speech that adults use when addressing young children. Its properties also change as a function of extralinguistic factors, such…
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel
Kevin Eloff, Okko Räsänen, Herman A. Engelbrecht +2
Multi-agent reinforcement learning has been used as an effective means to study emergent communication between agents, yet little focus has been given to continuous acoustic commun…
Analysis of Predictive Coding Models for Phonemic Representation Learning in Small Datasets
María Andrea Cruz Blandón, Okko Räsänen
Neural network models using predictive coding are interesting from the viewpoint of computational modelling of human language acquisition, where the objective is to understand how…
A computational model of early language acquisition from audiovisual experiences of young infants
Okko Räsänen, Khazar Khorrami
Earlier research has suggested that human infants might use statistical dependencies between speech and non-linguistic multimodal input to bootstrap their language learning before…
SylNet: An Adaptable End-to-End Syllable Count Estimator for Speech
Shreyas Seshadri, Okko Räsänen
Automatic syllable count estimation (SCE) is used in a variety of applications ranging from speaking rate estimation to detecting social activity from wearable microphones or devel…
Blind phoneme segmentation with temporal prediction errors
Paul Michel, Okko Räsänen, Roland Thiollière +1
Phonemic segmentation of speech is a critical step of speech recognition systems. We propose a novel unsupervised algorithm based on sequence prediction models such as Markov chain…