We show that a carefully crafted probabilistic morphological analyzer significantly outperforms a reasonable, naive baseline for Syriac. Syriac is an under-resourced Semitic language for which there are no available language tools such as morphological analyzers. Such tools are widely used to contribute to the process of annotating morphologically complex languages. We introduce and connect novel data-driven models for segmentation, dictionary linkage, and morphological tagging in a joint pipeline to create a probabilistic morphological analyzer requiring only labeled data. We explore the performance of this model with varying amounts of training data and find that with about 34,500 tokens, it can outperform the baseline trained on over 99,000 tokens and achieve an accuracy of just over 80%. When trained on all available training data, this joint model achieves 86.47% accuracy — a 29.7% reduction in error rate over the baseline.

<p>Automatsko prepoznavanje govora je tehnologija koja računarima<br />omogućava pretvaranje izgovorenih reči u tekst. Ona se može<br />primeniti u mnogim savremenim sistemima koji uključuju komunikaciju<br />između čoveka i mašine. U ovoj disertaciji detaljno je opisana jedna<br />od dve glavne komponente sistema za prepoznavanje govora, a to je<br />jezički model, koji specificira rečnik sistema, kao i pravila prema<br />kojim se pojedinačne reči mogu povezati u rečenicu. Srpski jezik spada<br />u grupu visoko inflektivnih i morfološki bogatih jezika, što znači<br />da koristi veći broj različitih završetaka reči za izražavanje<br />željene gramatičke, sintaksičke ili semantičke funkcije date reči.<br />Ovakvo ponašanje često dovodi do velikog broja grešaka sistema za<br />prepoznavanje govora kod kojih zbog dobrog akustičkog poklapanja<br />prepoznavač pogodi osnovni oblik reči, ali pogreši njen završetak.<br />Taj završetak može da označava drugu morfološku kategoriju, na<br />primer, padež, rod ili broj. U radu je predstavljen novi alat za<br />modelovanje jezika, koji uz identitet reči u modelu može da koristi<br />dodatna leksička i morfološka obeležja reči, čime je testirana<br />hipoteza da te dodatne informacije mogu pomoći u prevazilaženju<br />značajnog broja grešaka prepoznavača koje su posledica<br />inflektivnosti srpskog jezika.</p> / <p>Automatic speech recognition is a technology that allows computers to<br />convert spoken words into text. It can be applied in various areas which<br />involve communication between humans and machines. This thesis primarily<br />deals with one of two main components of speech recognition systems - the<br />language model, that specifies the vocabulary of the system, as well as the<br />rules by which individual words can be linked into sentences. The Serbian<br />language belongs to a group of highly inflective and morphologically rich<br />languages, which means that it uses a number of different word endings to<br />express the desired grammatical, syntactic, or semantic function of the given<br />word. Such behavior often leads to a significant number of errors in speech<br />recognition systems where due to good acoustic matching the recognizer<br />correctly guesses the basic form of the word, but an error occurs in the word<br />ending. This word ending may indicate a different morphological category, for<br />example, word case, grammatical gender, or grammatical number. The<br />thesis presents a new language modeling tool which, along with the word<br />identity, can also model additional lexical and morphological features of the<br />word, thus testing the hypothesis that this additional information can help<br />overcome a significant number of recognition errors that result from the high<br />inflectivity of the Serbian language.</p>