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
School common, ELEC, Department of Signal Processing and Acoustics, Speech Recognition, Centre of Excellence in Computational Inference, COIN
A user study to compare two conversational assistants designed for people with hearing impairments
Publishing year: 2019
Department of Signal Processing and Acoustics, Department of Neuroscience and Biomedical Engineering, Speech Recognition
Statistical models of morphology predict eye-tracking measures during visual word recognition
Publishing year: 2019
Centre of Excellence in Computational Inference, COIN, School common, ELEC, Speech Recognition, Department of Signal Processing and Acoustics
North Sámi morphological segmentation with low-resource semi-supervised sequence labeling
Publishing year: 2019
Department of Signal Processing and Acoustics, Speech Recognition, Centre of Excellence in Computational Inference, COIN
Building personalised speech technology systems with sparse, bad quality or out-of-domain data
Publishing year: 2019
Department of Signal Processing and Acoustics, Department of Neuroscience and Biomedical Engineering, Speech Recognition
Statistical models of morphology predict eye-tracking measures during visual word recognition
Publishing year: 2019
MEMORY AND COGNITION
Department of Signal Processing and Acoustics, Department of Communications and Networking, Helsinki Institute for Information Technology HIIT, Centre of Excellence in Computational Inference, COIN, User Interfaces, Speech Recognition
Computer-Supported Form Design using Keystroke-Level Modeling with Reinforcement Learning
Publishing year: 2019
Department of Signal Processing and Acoustics, Helsinki Institute for Information Technology HIIT, Department of Communications and Networking, Centre of Excellence in Computational Inference, COIN, User Interfaces, Speech Recognition
RL-KLM: Automating Keystroke-level Modeling with Reinforcement Learning
Publishing year: 2019
Speech Recognition, Department of Signal Processing and Acoustics
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling
Publishing year: 2019
Department of Signal Processing and Acoustics, Speech Recognition
Modern subword-based models for automatic speech recognition
Publishing year: 2019