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Speaker Recognition. By Afshan Hina. Overview. What is speaker recognition? Voice Pattern Categories of Speaker recognition. Verification versus identification Phases of speaker recognition Technology used Advantages and disadvantages Conclusion Commentary. Speaker Recognition.
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Speaker Recognition By Afshan Hina
Overview • What is speaker recognition? • Voice Pattern • Categories of Speaker recognition. • Verification versus identification • Phases of speaker recognition • Technology used • Advantages and disadvantages • Conclusion • Commentary
Speaker Recognition • Also referred as voiceprint recognition or voice recognition. • Identifies sound. • Such systems extract features from speech, model them and use them to recognize the person from his/her voice. • Differs from speech recognition- where words are identified and not speaker. • r eh k ao g n ay z s p iy ch "recognize speech" • r eh k ay n ay s b iy ch” "wreck a nice beach"
Voice patterns • uses the acoustic features of speech that have been found to differ between individuals. • These acoustic patterns reflect both anatomy and learned behavioral patterns
Voice patterns • But the data used in a voiceprint is a sound spectrogram, not a wave form. • Spectrograms also use colors or shades of grey to represent the acoustical qualities of sound.
Categories • Field text • Text dependant • Text independent • Conversational.
Verification versus identification • Speaker verification: If the speaker claims to be of a certain identity and the voice is used to verify this claim. Is usually used in applications which require secure access. Its a 1:1 match. • Speaker identification: is the task of determining an unknown speaker's identity. It’s a 1:N match
Phases of speaker recognition • Enrollment Phase: During enrollment the speaker's voice is recorded and typically a number of features are derived to form a voice print, template, or model. • Test Phase: In the test phase (also called verification or identification phase) the speaker's voice is matched to the templates or models
Technologies used: • frequency estimation • hidden Markov models • pattern matching algorithms • neural networks • matrix representation • decision trees
Advantages • Remote authentication over legacy phone line. • Users do not have to remember passwords or pass phrases. • Users do not have to go through separate process of verification.
Disadvantages • Misspoken or misread prompted phrases. • Extreme emotional states • Time varying microphone placement. • Poor or inconsistent room acoustics • Channel mismatch • Sickness • Aging.
Conclusion • speaker verification is a pervasive low cost way of including a biometric check. • does not require specialized equipment to use the system. • It provides a very strong binding between the presented credential (voice) and the user.
Commentary • Although speaker verification performance can be affected by various human and external factors, it does provide a powerful authentication solution.