Machine Classification of Spoken Language is basic research involving the classification and identification of the speech patterns of native and non-native speakers of English using phonetic and phonological analyses. Biometric procedures, such as retinal and fingerprint scanners, can identify individuals, but little has been done to incorporate readily available linguistic evidence from spoken language into the classification of individuals. Currently, no system is available to warfighters and security personnel to use this linguistic information during interrogations and interactions at military checkpoints and airport terminals. The initial phase of this research involved the hand-coding of phonetic and phonological rules to determine speakers' native accents from passages spoken in English. The research progresses into the Automatic Dialect Identification project that provides machine-driven phonological analyses with confidence levels to identify the native language/dialect of both native and non-native speakers of English. Machine learning techniques will be used to train the phonological analysis engine in order to automate the procedure.
This NRL base-funded is basic research under funding document WX30002.
Dr. Dennis Perzanowski
Interactive Systems, Code 5512
Naval Research Laboratory
Washington DC 20375
Email: w5512 "at" aic.nrl.navy.mil