Publications

See also Google Scholar.

• Seppo Enarvi, Marilisa Amoia, Miguel Del-Agua Teba, Brian Delaney, Frank Diehl, Guido Gallopyn, Stefan Hahn, Kristina Harris, Liam McGrath, Yue Pan, Joel Pinto, Luca Rubini, Miguel Ruiz, Gagandeep Singh, Fabian Stemmer, Weiyi Sun, Paul Vozila, Thomas Lin, and Ranjani Ramamurthy (2020), Generating Medical Reports from Patient-Doctor Conversations using Sequence-to-Sequence Models. In Proceedings of the First Workshop on Natural Language Processing for Medical Conversations. NLPMC Best Paper Award. (PDF, BibTex).
• Seppo Enarvi (2017), Modeling Conversational Finnish for Automatic Speech Recognition. Doctoral dissertation, Aalto University, Espoo. (PDF, BibTex)
• Maria Uther, Anna-Riikka Smolander, Katja Junttila, Mikko Kurimo, Reima Karhila, Seppo Enarvi, Sari Ylinen (2018), User Experiences from Children Using a Speech Learning Application: Implications for Designers of Speech Training Applications for Children. In Proceedings of EuroCALL 2018.
• Peter Smit, Siva Reddy Gangireddy, Seppo Enarvi, Sami Virpioja, and Mikko Kurimo (2017), Character-Based Units for Unlimited Vocabulary Continuous Speech Recognition. In Proceedings of the 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 149–156. (PDF, BibTex).
• Peter Smit, Siva Reddy Gangireddy, Seppo Enarvi, Sami Virpioja, and Mikko Kurimo (2017), Aalto System for the 2017 Arabic Multi-Genre Broadcast Challenge. In Proceedings of the 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 338–345. (PDF, BibTex).
• Seppo Enarvi, Peter Smit, Sami Virpioja, and Mikko Kurimo (2017), Automatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies. In IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(11), pp. 2085–2097. (PDF, BibTex).
  NOTE: See erratum at the end of the PDF file.
• Mikko Kurimo, Seppo Enarvi, Ottokar Tilk, Matti Varjokallio, André Mansikkaniemi, and Tanel Alumäe (2017), Modeling under-resourced languages for speech recognition. Language Resources and Evaluation, 51(4), pp. 961–987. (PDF, BibTex).
  NOTE: See erratum at the end of the PDF file.
  RELATED CODE: filter-text, filter-dictionary.
• Seppo Enarvi and Mikko Kurimo (2016), TheanoLM — An Extensible Toolkit for Neural Network Language Modeling. In Proceedings of the 17th Annual Conference of the International Speech Communication Association (INTERSPEECH), pp. 3052–3056. (PDF, BibTex).
  RELATED CODE: TheanoLM.
• Seppo Enarvi and Mikko Kurimo (2013), Studies on Training Text Selection for Conversational Finnish Language Modeling. In Proceedings of the 10th International Workshop on Spoken Language Translation (IWSLT), pp. 256–263. (PDF, BibTex).
  RELATED CODE: filter-text, oov-stats.
• Seppo Enarvi and Mikko Kurimo (2013), A Novel Discriminative Method for Pruning Pronunciation Dictionary Entries. In Proceedings of the 7th Conference on Speech Technology and Human-Computer Dialogue (SpeD), pp. 113–116. (PDF, BibTex).
  NOTE: See erratum at the end of the PDF file.
  RELATED CODE: filter-dictionary.
• Seppo Enarvi (2012), Finnish Language Speech Recognition for Dental Health Care. Licentiate thesis, Aalto University School of Science, Espoo. (PDF, BibTex)
• Seppo Enarvi (2006), Image-based detection of defective logs. Master’s thesis, Helsinki University of Technology, Espoo. (PDF, BibTex)