Jira: a Kurdish Speech Recognition System Designing and Building Speech
  Corpus and Pronunciation Lexicon
  In this paper, we introduce 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. To fill this gap, we introduce
the first speech corpus and pronunciation lexicon for the Kurdish language.
Regarding speech corpus, we designed a sentence collection in which the ratio
of di-phones in the collection resembles the real data of the Central Kurdish
language. The designed sentences are uttered by 576 speakers in a controlled
environment with noise-free microphones (called AsoSoft Speech-Office) and in
Telegram social network environment using mobile phones (denoted as AsoSoft
Speech-Crowdsourcing), resulted in 43.68 hours of speech. Besides, a test set
including 11 different document topics is designed and recorded in two
corresponding speech conditions (i.e., Office and Crowdsourcing). Furthermore,
a 60K pronunciation lexicon is prepared in this research in which we faced
several challenges and proposed solutions for them. The Kurdish language has
several dialects and sub-dialects that results in many lexical variations. Our
methods for script standardization of lexical variations and automatic
pronunciation of the lexicon tokens are presented in detail. To setup the
recognition engine, we used the Kaldi toolkit. A statistical tri-gram language
model that is extracted from the AsoSoft text corpus is used in the system.
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 best performance achieved
by the SGMM acoustic model which results in 13.9% of the average word error
rate (on different document topics) and 4.9% for the general topic.
