Voice Modeling Methods

Building a voice model means to capture the characteristics of a speaker's voice in a data structure. This data structure is then used by a computer for further processing, such as comparison with other voices. Voice modeling is a vital step in the process of automatic speaker recognition that itself is the foundation of several applied technologies: (a) biometric authentication, (b) speech recognition and (c) multimedia indexing. Current automatic speaker recognition works well under relatively constrained circumstances, such as studio recordings, or when prior knowledge on the number and identity of occurring speakers is available. Under more adverse conditions, such as in feature films or amateur material on the web, the achieved speaker recognition scores drop below a rate that is acceptable for an end user or for further processing. In this book, algorithmic and methodic improvements to the state of the art in automatic speaker recognition are presented. They are accompanied by a capacious software toolkit called "sclib". Additionally, the method of "Eidetic Design" facilitates intuitive algorithm design, development and teaching.

Autorentext

Thilo was born in Lemgo/Germany in the 1980's and still loves themusic of this time. Maybe this is why he choose to analyzeacoustic data in his doctoral studies? When he's not playingmusic or doing research and development, he's probably exploringsome new fun sport together with his wife or thinks about god andhis world.

Klappentext

Building a voice model means to capture the characteristics of a speaker's voice in a data structure. This data structure is then used by a computer for further processing, such as comparison with other voices. Voice modeling is a vital step in the process of automatic speaker recognition that itself is the foundation of several applied technologies: (a) biometric authentication, (b) speech recognition and (c) multimedia indexing. Current automatic speaker recognition works well under relatively constrained circumstances, such as studio recordings, or when prior knowledge on the number and identity of occurring speakers is available. Under more adverse conditions, such as in feature films or amateur material on the web, the achieved speaker recognition scores drop below a rate that is acceptable for an end user or for further processing. In this book, algorithmic and methodic improvements to the state of the art in automatic speaker recognition are presented. They are accompanied by a capacious software toolkit called "sclib". Additionally, the method of "Eidetic Design" facilitates intuitive algorithm design, development and teaching.