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.
翻译:在本文中,我们为中央库尔德语引入了第一个名为 Jira 的大型词汇语音识别系统(LVSR ) 。库尔德语是一些国家3 000多万人使用的印度语欧洲语言,但由于缺乏语言和文字资源,没有语言识别系统。为填补这一空白,我们为库尔德语引入了第一个语音资料和读音词汇系统。关于语音资料,我们设计了一个词汇集,其中二个手机在收藏中的比例与中央库尔德语的真实数据相似。所设计的句子由576个发言者在有控制的环境下用无噪音的语音麦克风(称为AsoSoSoft语音办公室)和Telegram社会网络环境中使用手机(称为AsoSoft语音-Crowdform) 语言识别系统。此外,一个包含11个不同文件主题的测试集被设计并记录在两种相应的语音模型(即办公室和Crowdformormorm ) 。此外,一个60K pronciation 字典是来自本次研究的,其中我们面临数种硬体机机数据变换的, 包括了数个硬体变数的硬体数据。