New PDF release: Fundamentals of Speaker Recognition

By Homayoon Beigi

An rising know-how, Speaker acceptance is changing into recognized for offering voice authentication over the phone for helpdesks, name centres and different firm companies for enterprise strategy automation.

"Fundamentals of Speaker popularity" introduces Speaker id, Speaker Verification, Speaker (Audio occasion) category, Speaker Detection, Speaker monitoring and extra. The technical difficulties are carefully outlined, and a whole photograph is made up of the relevance of the mentioned algorithms and their utilization in construction a finished Speaker popularity process.

Designed as a textbook with examples and workouts on the finish of every bankruptcy, "Fundamentals of Speaker attractiveness" is appropriate for advanced-level scholars in machine technological know-how and engineering, targeting biometrics, speech reputation, trend reputation, sign processing and, particularly, speaker reputation. it's also a helpful reference for builders of business expertise and for speech scientists.

Please click the hyperlink less than "Additional details" to view supplemental details together with the desk of Contents and Index.

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An rising know-how, Speaker attractiveness is turning into famous for delivering voice authentication over the phone for helpdesks, name centres and different firm companies for enterprise technique automation. "Fundamentals of Speaker reputation" introduces Speaker identity, Speaker Verification, Speaker (Audio occasion) class, Speaker Detection, Speaker monitoring and extra.

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Extra resources for Fundamentals of Speaker Recognition

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1 . . . . . . . . . . . . . . 1 The Hypothetical Menagerie – an animal analogy of the behavior of speakers, defined by [21] . . . . . . . . . . . . . . . . . . 2 The Biometric Menagerie – an animal analogy of the behavior of speakers, defined by [54, 55] . . . . . . . . . . . . . . . . . 1 Terminology analogs of the spectral domain, used in the cepstral domain . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Mobile technology subscribers, worldwide, in the second quarter of 2009 according to GSMA [8] .

32 Mandarin Word, Ma (Horse) . . . . . . . . . . . . . . . . . 33 Mandarin Word, Ma (Scold) . . . . . . . . . . . . . . . . . 34 construct of a typical syllable, [tip] . . . . . . . . . . . . . . . 29 Pitch versus Frequency for frequencies of up to 1000 Hz . . . . . . 147 Pitch versus Frequency for the entire audible range . . . . . . . . 147 Block Diagram of a typical Sampling Process for Speech – Best Alternative .

7 Top: c1 and c2 after RASTA Liftering; Bottom: Power Spectral Density of c1 after RASTA Liftering . . . . . . . . . . . . . . 8 Top: c1 and c2 after CMN and ARMA Liftering; Bottom: Power Spectral Density of c1 after CMN and ARMA Liftering . . . . . . 9 Adult male (44 years old) . . . . . . . . . . . . . . . . . . . 10Male child (2 years old) . . . . . . . . . . . . . . . . . . . 11Impulse response of the shortpass lifter .

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