Formant analysis of vowels in Kurdish language

Dinler, Özlem Batur; Karabiber, Fatih

doi:10.1109/siu.2017.7960265

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2020

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

KLPT – Kurdish Language Processing Toolkit

Ahmadi¹

2020

Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)

View full text Add to dashboard Cite

Despite the recent advances in applying language-independent approaches to various natural language processing tasks thanks to artificial intelligence, some language-specific tools are still essential to process a language in a viable manner. Kurdish language is a lessresourced language with a notable diversity in dialects and scripts and lacks basic language processing tools. To address this issue, we introduce a language processing toolkit to handle such a diversity in an efficient way. Our toolkit is composed of fundamental components such as text preprocessing, stemming, tokenization, lemmatization and transliteration and is able to get further extended by future developers. This project is publicly available 1 .

show abstract

KLPT – Kurdish Language Processing Toolkit

Ahmadi¹

2020

Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)

View full text Add to dashboard Cite

show abstract

Kurdish Spoken Letter Recognition based on k-NN and SVM Model

Abdul¹

2020

JUOR

View full text Add to dashboard Cite

Automatic recognition of spoken letters is one of the most challenging tasks in the area of speech recognition system. In this paper, different machine learning approaches are used to classify the Kurdish alphabets such as SVM and k-NN where both approaches are fed by two different features, Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficients (MFCCs). Moreover, the features are combined together to learn the classifiers. The experiments are evaluated on the dataset that are collected by the authors as there as not standard Kurdish dataset. The dataset consists of 2720 samples as a total. The results show that the MFCC features outperforms the LPC features as the MFCCs have more relative information of vocal track. Furthermore, fusion of the features (MFCC and LPC) is not capable to improve the classification rate significantly.

show abstract

Formant analysis of vowels in Kurdish language

Cited by 2 publications

References 3 publications

KLPT – Kurdish Language Processing Toolkit

KLPT – Kurdish Language Processing Toolkit

Kurdish Spoken Letter Recognition based on k-NN and SVM Model

Contact Info

Product

Resources

About