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/).

Group members

Mikko Kurimo

Professori (Associate professor)
[email protected]
+358503476221

Katri Leino

Tohtorikoulutettava
[email protected]

Aku Rouhe

Doctoral candidate
[email protected]

Anja Virkkunen

Tohtorikoulutettava
[email protected]

Juho Leinonen

Doctoral candidate
[email protected]

Mittul Singh

Postdoctoral researcher
[email protected]
+358505147551

Hemant Kathania

Postdoctoral researcher
[email protected]
+358503420092

Tamás Grósz

Postdoctoral researcher
[email protected]
+358417044324

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

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

Machine translation into morphologically rich low-resource languages

Stig-Arne Grönroos 2020

Using Fan-Made Content, Subtitles and Face Recognition for Character-Centric Video Summarization

Ismail Harrando, Alison Reboud, Pasquale Lisena, Raphaël Troncy, Jorma Laaksonen, Anja Virkkunen, Mikko Kurimo 2020 Proceedings of the TRECVID 2020 Workshop

Finnish ASR with deep transformer models

Abhilash Jain, Aku Rouhe, Stig Arne Grönroos, Mikko Kurimo 2020 Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Data augmentation using prosody and false starts to recognize non-native children's speech

Hemant Kathania, Mittul Singh, Tamás Grósz, Mikko Kurimo 2020 Proceedings of Interspeech

Study of Formant Modification for Children ASR

Hemant Kathania, Sudarsana Kadiri, Paavo Alku, Mikko Kurimo 2020 Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing

Brain activity reflects the predictability of word sequences in listened continuous speech

Miika Koskinen, Mikko Kurimo, Joachim Gross, Aapo Hyvärinen, Riitta Hari 2020 NeuroImage

FinChat: Corpus and evaluation setup for Finnish chat conversations on everyday topics

Katri Leino, Juho Leinonen, Mittul Singh, Sami Virpioja, Mikko Kurimo 2020 Proceedings of Interspeech
More information on our research in the Research database.
Research database
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