Abstract
Conversational telephone speech (CTS) collections of Arabic dialects distributed trough the Linguistic Data Consortium (LDC) provide an invaluable resource for the development of robust speech systems including speaker and speech recognition, translation, spoken dialogue modeling, and information summarization. They are frequently relied on also in language (LID) and dialect identification (DID) evaluations. The first part of this study attempts to identify the source of the relatively high DID performance on LDC’s Arabic CTS corpora seen in recent literature. It is found that recordings of each dialect exhibit unique channel and noise characteristics and that silence regions are sufficient for performing reasonably accurate DID. The second part focuses on phonotactic dialect modeling that utilizes phone recognizers and support vector machines (PRSVM). A simple N-gram normalization of PRSVM input supervectors utilizing hard limiting is introduced and shown to outperform the standard approach used in current LID and DID systems.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
