Department of Signal Processing and Acoustics

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.
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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/).

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

Advances in subword-based HMM-DNN speech recognition across languages

Peter Smit, Sami Virpioja, Mikko Kurimo 2021 Computer Speech and Language

Morphologically motivated word classes for very large vocabulary speech recognition of Finnish and Estonian

Matti Varjokallio, Sami Virpioja, Mikko Kurimo 2021 Computer Speech and Language

Graph-based Syntactic Word Embeddings

Ragheb Al-Ghezi, Mikko Kurimo 2020 Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)

Applying dnn adaptation to reduce the session dependency of ultrasound tongue imaging-based silent speech interfaces

Gábor Gosztolya, Tamás Grósz, László Tóth, Alexandra Markó, Tamás Gábor Csapó 2020 ACTA POLYTECHNICA HUNGARICA

Visual Interpretation of DNN-based Acoustic Models using Deep Autoencoders

Tamás Grósz, Mikko Kurimo 2020 Machine Learning Methods in Visualisation for Big Data

Morfessor EM+Prune: Improved subword segmentation with expectation maximization and pruning

Stig Arne Grönroos, Sami Virpioja, Mikko Kurimo 2020 Proceedings of The 12th Language Resources and Evaluation Conference

Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation

Stig-Arne Grönroos, Sami Virpioja, Mikko Kurimo 2020 MACHINE TRANSLATION
More information on our research in the Research database.
Research database
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