| Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits J Pohjalainen, O Räsänen, S Kadioglu Computer Speech & Language 29 (1), 145-171, 2015 | 265 | 2015 |
| A thorough evaluation of the Language Environment Analysis (LENA) system A Cristia, M Lavechin, C Scaff, M Soderstrom, C Rowland, O Räsänen, ... Behavior research methods 53, 467-486, 2021 | 119 | 2021 |
| Automatic posture and movement tracking of infants with wearable movement sensors M Airaksinen, O Räsänen, E Ilén, T Häyrinen, A Kivi, V Marchi, A Gallen, ... Scientific reports 10 (1), 169, 2020 | 104 | 2020 |
| Random subset feature selection in automatic recognition of developmental disorders, affective states, and level of conflict from speech. O Räsänen, J Pohjalainen Interspeech, 210-214, 2013 | 104 | 2013 |
| An improved speech segmentation quality measure: the r-value OJ Räsänen, UK Laine, T Altosaar Tenth Annual Conference of the International Speech Communication Association, 2009 | 101 | 2009 |
| Sequence prediction with sparse distributed hyperdimensional coding applied to the analysis of mobile phone use patterns OJ Räsänen, JP Saarinen IEEE transactions on neural networks and learning systems 27 (9), 1878-1889, 2015 | 99 | 2015 |
| Unsupervised word discovery from speech using automatic segmentation into syllable-like units O Räsänen, G Doyle, MC Frank Interspeech–2015, 2015 | 87 | 2015 |
| Pre-linguistic segmentation of speech into syllable-like units O Räsänen, G Doyle, MC Frank Cognition 171, 130-150, 2018 | 84 | 2018 |
| A joint model of word segmentation and meaning acquisition through cross-situational learning. O Räsänen, H Rasilo Psychological review 122 (4), 792, 2015 | 72 | 2015 |
| Computational modeling of phonetic and lexical learning in early language acquisition: Existing models and future directions O Räsänen Speech Communication 54 (9), 975-997, 2012 | 71 | 2012 |
| ALICE: An open-source tool for automatic measurement of phoneme, syllable, and word counts from child-centered daylong recordings O Räsänen, S Seshadri, M Lavechin, A Cristia, M Casillas Behavior Research Methods 53, 818-835, 2021 | 57 | 2021 |
| Modeling dependencies in multiple parallel data streams with hyperdimensional computing O Räsänen, S Kakouros IEEE Signal Processing Letters 21 (7), 899-903, 2014 | 53 | 2014 |
| Is infant-directed speech interesting because it is surprising?–Linking properties of IDS to statistical learning and attention at the prosodic level O Räsänen, S Kakouros, M Soderstrom Cognition 178, 193-206, 2018 | 47 | 2018 |
| A computational model of word segmentation from continuous speech using transitional probabilities of atomic acoustic events O Räsänen Cognition 120 (2), 149-176, 2011 | 46 | 2011 |
| Development of a novel robust measure for interhemispheric synchrony in the neonatal EEG: activation synchrony index (ASI) O Räsänen, M Metsäranta, S Vanhatalo Neuroimage 69, 256-266, 2013 | 43 | 2013 |
| Interhemispheric synchrony in the neonatal EEG revisited: activation synchrony index as a promising classifier N Koolen, A Dereymaeker, O Räsänen, K Jansen, J Vervisch, V Matic, ... Frontiers in human neuroscience 8, 1030, 2014 | 40 | 2014 |
| Perception of sentence stress in speech correlates with the temporal unpredictability of prosodic features S Kakouros, O Räsänen Cognitive science 40 (7), 1739-1774, 2016 | 39 | 2016 |
| Early development of synchrony in cortical activations in the human N Koolen, A Dereymaeker, O Räsänen, K Jansen, J Vervisch, V Matic, ... Neuroscience 322, 298-307, 2016 | 37 | 2016 |
| A method for noise-robust context-aware pattern discovery and recognition from categorical sequences O Räsänen, UK Laine Pattern Recognition 45 (1), 606-616, 2012 | 33 | 2012 |
| Blind segmentation of speech using non-linear filtering methods O Räsänen, UK Laine, T Altosaar Speech Technologies, 105-124, 2011 | 30 | 2011 |
Unto K. LaineProfessor of Speech Technology, Aalto UniversityVerified email at aalto.fi
