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Most of the classic texts in Kurdish literature are poems. Knowing the meter of the poems is helpful for correct reading, a better understanding of the meaning, and avoiding ambiguity. This paper presents a rule-based method for the automatic classification of the poem meter for the Central Kurdish language also known as Sorani. The metrical system of Kurdish poetry is divided into three classes quantitative, syllabic, and free verses. As the vowel length is not phonemic in the language, there are uncertainties in syllable weight and meter identification. The proposed method generates all the possible situations and then, by considering all lines of the input poem and the common meter patterns of Kurdish poetry, identifies the most probable meter type and pattern of the input poem. Evaluation of the method on a dataset from VejinBooks Kurdish corpus resulted in 97.3% of precision in meter type and 96.2% of precision in pattern identification. [ABSTRACT FROM AUTHOR]

• Reviewed the phonology and alphabet of the Central Kurdish, a low-resourced language. • Proposed an adequate rule-based method for G2P conversion of the Central Kurdish. • Specified and ranked phonological constraints in the framework of Optimality Theory. • Showed that the current alphabet of Central Kurdish does not need additional letters. The writing system of Central Kurdish features three cases in which there is no one-to-one mapping between the orthographical letters and the phonemes of the language. Consequently, the written words including these cases may be pronounced in multiple ways. The process of finding the correct pronunciation of written words is called Grapheme-to-Phoneme (G2P) conversion and is a key step in natural language processing tasks such as speech synthesis. As Central Kurdish is a low-resourced language, we present a G2P conversion method based on the phonological rules of the language, rather than pronunciation dictionaries and data-driven learning methods. After reviewing the phonology and alphabet of the language through the framework of Optimality Theory, we generate all possible pronunciations. Then, by specifying and applying ranked constraints, we eliminate undesirable candidates so as to keep only one well-formed pronunciation per word. The evaluation of our proposed method on two datasets resulted in 0.75% of overall Phoneme Error Rate (PER) and achieved 94.71% precision in the detection of the short vowel /i/ and 100% of accuracy in the conversion of the letters "ی" and "و". Analyzing these results suggests that there is no need for additional new letters in the current orthographic system of Central Kurdish. This approach also enables us to have a ranked suggestion list for the manual checking of the few unresolved ambiguous situations. [ABSTRACT FROM AUTHOR]

This paper introduces the first large vocabulary speech recognition system (LVSR) for the Central Kurdish language, named Jira. The Kurdish language is an Indo-European language spoken by more than 30 million people in several countries, but due to the lack of speech and text resources, there is no speech recognition system for this language. We introduce the first speech corpus and pronunciation lexicon for the Central Kurdish language to fill this gap. Regarding speech corpus, we designed a sentence collection and the sentences are uttered by 576 speakers in a controlled environment with noise-free microphones [called AsoSoft Speech-Office (A part of this corpus is available for public access at https://github.com/AsoSoft/AsoSoft-Speech-Corpus)] and in Telegram social network environment using mobile phones (denoted as AsoSoft Speech-Crowdsourcing), resulting in 43.68 h of speech. Besides, a test set including 11 different document topics is designed and recorded in two corresponding speech conditions [i.e., Office (The office test set is available online at https://github.com/AsoSoft/AsoSoft-Speech-Testset) and Crowdsourcing]. Furthermore, a 60 K pronunciation lexicon is prepared in this research in which we faced several challenges and proposed solutions for them. Our methods for script standardization of lexical variations and automatic pronunciation of the lexicon tokens are presented in detail. To set up the recognition engine, we used the Kaldi toolkit. A statistical tri-gram language model is used and several standard recipes including HMM-based models (i.e., mono, tri1, tr2, tri2, tri3), SGMM, and DNN methods are used to generate the acoustic model. These methods are trained with AsoSoft Speech-Office and AsoSoft Speech-Crowdsourcing and a combination of them. The SGMM acoustic model achieved the best performance, which results in 13.9% of the average word error rate (on different document topics) and 4.9% for the general topic. [ABSTRACT FROM AUTHOR]