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
ELEC-E5521 Speech and Language Processing Methods
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
Gaming enhances learning-induced plastic changes in the brain
Katja Junttila, Anna Riikka Smolander, Reima Karhila, Anastasia Giannakopoulou, Maria Uther, Mikko Kurimo, Sari Ylinen
2022
Brain and Language
A Formant Modification Method for Improved ASR of Children’s Speech
Hemant Kathania, Sudarsana Kadiri, Paavo Alku, Mikko Kurimo
2022
Speech Communication
Lahjoita puhetta: a large-scale corpus of spoken Finnish with some benchmarks
Anssi Moisio, Dejan Porjazovski, Aku Rouhe, Yaroslav Getman, Anja Virkkunen, Ragheb AlGhezi, Mietta Lennes, Tamás Grósz, Krister Lindén, Mikko Kurimo
2022
LANGUAGE RESOURCES AND EVALUATION
Self-supervised end-to-end ASR for low resource L2 Swedish
Ragheb Al-Ghezi, Yaroslav Getman, Aku Rouhe, Raili Hildén, Mikko Kurimo
2021
22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
LSTM-XL: Attention Enhanced Long-Term Memory for LSTM Cells
Tamás Grósz, Mikko Kurimo
2021
Text, Speech, and Dialogue - 24th International Conference, TSD 2021, Proceedings
Synthesis Speech Based Data Augmentation for Low Resource Children ASR
Virender Kadyan, Hemant Kathania, Prajjval Govil, Mikko Kurimo
2021
Speech and Computer - 23rd International Conference, SPECOM 2021, Proceedings
Speaker Verification Experiments for Adults and Children Using Shared Embedding Spaces
Tuomas Kaseva, Hemant Kathania, Aku Rouhe, Mikko Kurimo
2021
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), May 31-June 2, 2021
Spectral modification for recognition of children’s speech under mismatched conditions
Hemant Kathania, Sudarsana Kadiri, Paavo Alku, Mikko Kurimo
2021
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)
Using data augmentation and time-scale modification to improve ASR of children’s speech in noisy environments
Hemant Kathania, Sudarsana Kadiri, Paavo Alku, Mikko Kurimo
2021
APPLIED SCIENCES
Vowel non-vowel based spectral warping and time scale modification for improvement in children’s ASR
Hemant Kathania, Avinash Kumar, Mikko Kurimo
2021
IEEE International Conference on Acoustics, Speech and Signal Processing