Speaker Verification Determine whether unknown speaker matches a specific speaker One-to-one mapping Close-set verification: The population of clients is fixed Open-set verification: New clients can be added without having to redesign the system Is this Bob' s voice?
Speaker Verification • Determine whether unknown speaker matches a specific speaker • One-to-one mapping • Close-set verification: The population of clients is fixed • Open-set verification: New clients can be added without having to redesign the system
Speaker diarization Determine when a speaker change has occurred in speech signal (segmentation) Group together speech segments corresponding to the same speaker( clustering) Prior speaker information may or may not be available Where are speaker Which segments are from changes? the same speaker?
Speaker diarization • Determine when a speaker change has occurred in speech signal (segmentation) • Group together speech segments corresponding to the same speaker (clustering) • Prior speaker information may or may not be available
Introduction: Generic Speaker Recognition System Basic structure of a speaker recognition system Unknow Analysis Feature Frames eatureVector Decision Speech Preprocessing Pattern Extraction Matching Enrollment Feature Preprocessing Extraction Speaker Models
Introduction: Generic Speaker Recognition System • Basic structure of a speaker recognition system Preprocessing Feature Extraction Pattern Matching Preprocessing Feature Extraction Speaker Models Unknow Speech Analysis Frames Feature Vector Enrollment Scoring Decision
Introduction Main research fields on sre Feature Extraction Pattern matching Scoring method
Introduction: Main Research Fields on SRE • Feature Extraction • Pattern matching • Scoring method
PROPERTIES OF DEAL FEATURES ideally a feature parameter should Nolan, 1983 show high between-speaker variability and low within-speaker variabili be resistant to attempted disguise or mimicry have a high frequency of occurrence in relevant materials be robust in transmission be relatively easy to extract and measure
PROPERTIES OF IDEAL FEATURES ideally a feature parameter should（F.Nolan，1983）： • show high between-speaker variability and low within-speaker variability • be resistant to attempted disguise or mimicry • have a high frequency of occurrence in relevant materials • be robust in transmission • be relatively easy to extract and measure