20 citations · 74 across the 21 of their papers we have counts for
25 papers
Benchmarking Adult Addressee Classification Across Child- and Adult-Directed Speech Datasets
Sofoklis Kakouros, Daniil Kocharov, Okko Räsänen
In this work, we present a comprehensive analysis of classification performance for distinguishing child-directed speech (CDS) from adult-directed speech (ADS) using speech data fr…
TSExplorer: An interactive data annotation and exploration tool for time-series data
Einari Vaaras, Manu Airaksinen, Okko Räsänen
We present TSExplorer, a cross-platform tool for interactive annotation and exploration of time-series data. The tool enables users to inspect high-dimensional datasets through mul…
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…
Deriving Benchmarking Datasets from Long-Form Recordings: Challenges and Opportunities
Kaveri K. Sheth, Lawrence Borst, Tarek Kunze +6
Long-form recordings (LFRs) of child-centered audio are ecologically valid sources for studying early language development, but three problems limit their use. First, LFR corpora a…
Evaluating Interactive 2D Visualization as a Sample Selection Strategy for Biomedical Time-Series Data Annotation
Einari Vaaras, Manu Airaksinen, Okko Räsänen
Reliable machine-learning models in biomedical settings depend on accurate labels, yet annotating biomedical time-series data remains challenging. Algorithmic sample selection may…
Enabling automatic transcription of child-centered audio recordings from real-world environments
Daniil Kocharov, Okko Räsänen, Azarias Galama
Longform audio recordings obtained with microphones worn by children-also known as child-centered daylong recordings-have become a standard method for studying children's language…
A model of early word acquisition based on realistic-scale audiovisual naming events
Khazar Khorrami, Okko Räsänen
Infants gradually learn to parse continuous speech into words and connect names with objects, yet the mechanisms behind development of early word perception skills remain unknown.…
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…
Simultaneous or Sequential Training? How Speech Representations Cooperate in a Multi-Task Self-Supervised Learning System
Khazar Khorrami, María Andrea Cruz Blandón, Tuomas Virtanen +1
Speech representation learning with self-supervised algorithms has resulted in notable performance boosts in many downstream tasks. Recent work combined self-supervised learning (S…
Crowdsourcing and Evaluating Text-Based Audio Retrieval Relevances
Huang Xie, Khazar Khorrami, Okko Räsänen +1
This paper explores grading text-based audio retrieval relevances with crowdsourcing assessments. Given a free-form text (e.g., a caption) as a query, crowdworkers are asked to gra…
Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors
Einari Vaaras, Manu Airaksinen, Sampsa Vanhatalo +1
The recently-developed infant wearable MAIJU provides a means to automatically evaluate infants' motor performance in an objective and scalable manner in out-of-hospital settings.…
On Negative Sampling for Contrastive Audio-Text Retrieval
Huang Xie, Okko Räsänen, Tuomas Virtanen
This paper investigates negative sampling for contrastive learning in the context of audio-text retrieval. The strategy for negative sampling refers to selecting negatives (either…
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition
Einari Vaaras, Manu Airaksinen, Okko Räsänen
When domain experts are needed to perform data annotation for complex machine-learning tasks, reducing annotation effort is crucial in order to cut down time and expenses. For case…
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…
Unsupervised Audio-Caption Aligning Learns Correspondences between Individual Sound Events and Textual Phrases
Huang Xie, Okko Räsänen, Konstantinos Drossos +1
We investigate unsupervised learning of correspondences between sound events and textual phrases through aligning audio clips with textual captions describing the content of a whol…
Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation
Khazar Khorrami, Okko Räsänen
Decades of research has studied how language learning infants learn to discriminate speech sounds, segment words, and associate words with their meanings. While gradual development…
Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment
Yuanyuan Liu, Nelly Penttilä, Tiina Ihalainen +3
Imprecise vowel articulation can be observed in people with Parkinson's disease (PD). Acoustic features measuring vowel articulation have been demonstrated to be effective indicato…
Evaluation of Audio-Visual Alignments in Visually Grounded Speech Models
Khazar Khorrami, Okko Räsänen
Systems that can find correspondences between multiple modalities, such as between speech and images, have great potential to solve different recognition and data analysis tasks in…
Comparison of end-to-end neural network architectures and data augmentation methods for automatic infant motility assessment using wearable sensors
Manu Airaksinen, Sampsa Vanhatalo, Okko Räsänen
Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis play…
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit
Einari Vaaras, Sari Ahlqvist-Björkroth, Konstantinos Drossos +1
Researchers have recently started to study how the emotional speech heard by young infants can affect their developmental outcomes. As a part of this research, hundreds of hours of…
Unsupervised Discovery of Recurring Speech Patterns Using Probabilistic Adaptive Metrics
Okko Räsänen, María Andrea Cruz Blandón
Unsupervised spoken term discovery (UTD) aims at finding recurring segments of speech from a corpus of acoustic speech data. One potential approach to this problem is to use dynami…
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…