In the Language Bank: Heikki Rasilo

Who are you?

I am Heikki Rasilo, a postdoc researcher in the at Vrije Universiteit Brussel, Belgium. I got my PhD as a joint degree between VUB and Aalto University in 2017. After working in the private sector for a couple of years, I received a research grant from , through the , for continuing my research.

What is your research topic?

Already from the beginning of my PhD studies, my main research focus has been on physical speech production and on its learning mechanisms. How do human children learn to articulate and imitate the speech of their parents while using their own vocal tracts of very different size and shape? The acoustic properties of adult and infant speech are different as well, and it is difficult to compare them directly. Nevertheless, children learn to articulate their mother tongue, and I am interested in whether the articulatory learning process can also affect the way in which we recognize and comprehend speech. Perhaps one of the reasons why we understand speech better than machines is that we know the physical mechanism through which speech is produced.

I am currently investigating whether the acoustic representations of speech that are formed in learning speech articulation could also be utilized in automatic speech recognition. The amount of recorded speech data that is required in order to train the world’s best speech recognizers is vast, and human children are not likely to encounter a similar amount of speech during their speech acquisition process. Therefore, it must be possible to learn to understand speech with smaller amounts of data, and physical articulation may play a role in the learning process.

How is your research related to Kielipankki?

In a study that was published last year, I trained a neural network to simultaneously recognize both phonemes and physical articulation from speech. The hypothesis was that the articulatory learning would shape the representations the network would learn, and these new representations could be helpful also when recognizing phonemes. For the experiment, I needed some recorded speech as well as articulatory information related to it. In the Language Bank of Finland, I found the that contained a sufficient amount of Finnish speech material including phonemic transcriptions. From the transcriptions, I was able to generate coarse synthetic articulatory data by using a Finnish speech synthesizer. The results of the experiment were promising – the articulatory learning did shape the speech representations in ways that can enhance phoneme recognition.

In my previous research, I have also used the (available via ) that consists of simple sentences and their orthographic transcriptions. With Academy Research Fellow , we used the corpus in order to investigate certain algorithms for learning word-meaning mappings, word segmentation and acoustic patterns related to words.

Publications related to Kielipankki

Rasilo, H. (2020). . Teoksessa S. Denison., M. Mack, Y. Xu, & B.C. Armstrong (Eds.), Proceedings of the 42nd Annual Conference of the Cognitive Science Society (pp. 2203–2209). Cognitive Science Society.

Rasilo, H. & Räsänen, O. (2017), . Speech Communication, 86, 1–23.

Räsänen, O. & Rasilo, H. (2015), . Psychological Review, 122(4), 792–829.

Rasilo, H. & Räsänen, O. (2015), . Proceedings of the 16th Annual Conference of the International Speech Communication Association (Interspeech 2015), Dresden, Germany, pp. 1561–1565.

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