About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Conference paper
Statistical model migration in speaker recognition
Abstract
In large-scale deployments of speaker recognition systems the potential for legacy problems increases as the evolving technology may require configuration changes in the system thus invalidating already existing user voice accounts. Unless the entire database of original speech waveform were stored, users need to reenroll to keep their accounts functional, which, however, may be expensive and commercially not acceptable. We define model migration as a conversion of obsolete models to new-configuration models without additional data and waveform requirements and investigate ways to achieve such a migration with minimum loss of system accuracy. As a proof-of-concept, an algorithm for statistical migration in the Maximum A-Posteriori framework is studied and evaluated experimentally using the NIST SRE-03 dataset. The migration step is discussed in a wider conceptual framework of Conversational Biometrics.