| Summary: | Automatic speech recognition will soon be a part of everyday life. Even today many people use the speech recognizer in their smartphones, whether it is Google Now or Siri. Commercial applications have existed for years for automatic dictation, and command-based voice user interfaces. The abundance of software divides languages in two; in well-resourced languages there is no shortage of products, while under-resourced languages might not even receive academic interest. In this thesis, an automatic speech recognizer is built for North Sami, which is a morphologically rich under-resourced language in the Uralic family. These properties create challenges for the recognition process, of which this thesis will concentrate on the issue of out-of-vocabulary words. The use of whole words is compared with word fragments, morphs, and tests are conducted to optimize other language model variables such as vocabulary size and context length. The experiments show that morph-based language models solve the problem of out-of-vocabulary words and significantly improve the recognition results without slowing the process too much. In addition, increasing context length improves the morph models, while adding supervision to generating them does not. As such, this thesis recommends a high order morph model generated with unsupervised methods to be used with North Sami. Automaattinen puheentunnistus on piakkoin osa kaikkien ihmisten arkea. Jo nyt monet käyttävät älypuhelimissa mukana olevaa puheentunnistinta, oli kyse sitten Google Now:sta tai Siristä. Kaupallisia sovelluksia on ollut jo vuosia automaattisen sanelun, sekä käskypohjaisen käyttöliittymän toteutukseen. Tuotteiden runsaus jakaa kielet osiin, suuriresurssisissa kielissä kaupallisista sovelluksista ei ole pulaa, kun taas vähäresurssiset kielet eivät välttämättä saa edes akateemista huomiota. Tässä diplomityössä rakennetaan automaattinen puheentunnistin pohjoissaamelle, joka on vähäresurssinen ja morfologisesti rikas uralilaiseen kieliperheeseen kuuluva kieli. Nämä ...
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