Multi-scenario dataset for speaker recognition.

The present work describes different research techniques for collecting and organizing speech database in different scenario at the institute and successfully structuring the text independent speaker identification database in Indian context. In order to get the Multi-Scenario dataset, each speaker performed multiple sessions recording in reading style with English and Hindi language with same passages but under different conditions. This work analyzed different scenario affecting the performance of speaker recognition system when tested under dissimilar training conditions. Here four different scenarios are considered; sensor and environment, language, aging and health. To study the effect of sensor, language and environment on the performance of ASR system a database of 200 speaker was created. Under different environmental conditions, four different types of sensors in parallel configuration were used to study the sensor mismatch conditions over testing and training phase. The database contains speech samples of the individual in English and Hindi in read speech styles under two environment i.e. a controlled recording chamber and library. To study the aging effect, an aging NSIT speaker database (AG-NSIT-SD) of 53 famous personalities was collected from online source varying over a period of 10–20 years. Further to study the effect of health, a cough and cold NSIT speaker database (CC-NSIT-SD) of 38 speakers was also collected to study the performance of system. Apart from this, the effect of different noise types on the speaker identification was also studied on different sensors. [ABSTRACT FROM AUTHOR]