Speech recognition
Our goal is to generally improve the speech recognition methodology with the help of the new algorithms developed in Aalto University. Speech recognition offers challenging benchmarking tasks for efficient algorithms that can process and learn to represent large quantities of data. In addition to improving the acoustic models of phonemes we aim at developing new learning statistical language models for difficult large vocabulary continuous speech recognition tasks.
Research Overview
We currently specialize in the following research areas in speech recognition:
- Sub-word units and deep learning in language modeling
- Speaker adaptation and pronunciation rating in acoustic modeling
- Unlimited vocabulary continuous speech recognition
- Speech recognition and language modeling methods for under-resourced languages
- Methods for describing and translating audiovisual
- Speaker and language recognition and diarization
We are part of Finnish Center of Artificial Intelligence (FCAI, https://fcai.fi/).
Teaching
We are teaching the following courses:
ELEC-E5550 Statistical Natural Language Processing
Group members
Software & Demonstrations
Software produced as part of our research is available on our GitHub
Demonstration videos of our research work can be watched on our YouTube Channel
Latest publications
Automatic Rating of Spontaneous Speech for Low-Resource Languages
Ragheb Al-Ghezi, Yaroslav Getman, Ekaterina Voskoboinik, Mittul Singh, Mikko Kurimo
2023
2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings
Developing an AI-assisted Low-resource Spoken Language Learning App for Children
Yaroslav Getman, Nhan Phan, Ragheb Al-Ghezi, Ekaterina Voskoboinik, Mittul Singh, Tamas Grosz, Mikko Kurimo, Giampiero Salvi, Torbjorn Svendsen, Sofia Strombergsson, Anna Smolander, Sari Ylinen
2023
IEEE Access
Multi-task wav2vec2 Serving as a Pronunciation Training System for Children
Yaroslav Getman, Ragheb Al-Ghezi, Tamas Grosz, Mikko Kurimo
2023
9th Workshop on Speech and Language Technology in Education (SLaTE)
Investigating wav2vec2 context representations and the effects of fine-tuning, a case-study of a Finnish model
Tamas Grosz, Yaroslav Getman, Ragheb Al-Ghezi, Aku Rouhe, Mikko Kurimo
2023
Proceedings of Interspeech 2023
Non-game like training benefits spoken foreign-language processing in children with dyslexia
Katja Junttila, Anna Riikka Smolander, Reima Karhila, Mikko Kurimo, Sari Ylinen
2023
FRONTIERS IN HUMAN NEUROSCIENCE
Multilingual TTS Accent Impressions for Accented ASR
Georgios Karakasidis, Nathaniel Robinson, Yaroslav Getman, Atieno Ogayo, Ragheb Al-Ghezi, Ananya Ayasi, Shinji Watanabe, David R. Mortensen, Mikko Kurimo
2023
Text, Speech, and Dialogue - 26th International Conference, TSD 2023, Proceedings
A pronunciation Scoring System Embedded into Children’s Foreign Language Learning Games with Experimental Verification of Learning Benefits
Reima Karhila, Sari Ylinen, Anna-Riikka Smolander, Aku Rouhe, Ragheb Al-Ghezi, Yaroslav Getman, Tamas Grosz, Maria Uther, Mikko Kurimo
2023
9th Workshop on Speech and Language Technology in Education (SLaTE)
Spectral warping based data augmentation for low resource children’s speaker verification
Hemant Kumar Kathania, Virender Kadyan, Sudarsana Reddy Kadiri, Mikko Kurimo
2023
Multimedia Tools and Applications
New data, benchmark and baseline for L2 speaking assessment for low-resource languages
Mikko Kurimo, Yaroslav Getman, Ekaterina Voskoboinik, Ragheb Al-Ghezi, Heini Kallio, Mikko Kuronen, Anna von Zansen, Raili Hilden, Sirkku Kronholm, Ari Huhta, Krister Lindén
2023
Proceedings of 9th Workshop on Speech and Language Technology in Education (SLaTE)
Evaluating Morphological Generalisation in Machine Translation by Distribution-Based Compositionality Assessment
Anssi Moisio, Mathias Creutz, Mikko Kurimo
2023
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
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