Topic
Speaker recognition
About: Speaker recognition is a research topic. Over the lifetime, 14990 publications have been published within this topic receiving 310061 citations.
Papers published on a yearly basis
Papers
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01 Apr 1976
TL;DR: The paper indudes a discussion of the speaker-dependent properties of the speech signal, methods for selecting an efficient set of speech measurements, results of experimental studies illustrating the performance of various methods of speaker recognition, and a comparision of theperformance of automatic methods with that of human listeners.
Abstract: This paper presents a survey of automatic speaker recognition techniques. The paper indudes a discussion of the speaker-dependent properties of the speech signal, methods for selecting an efficient set of speech measurements, results of experimental studies illustrating the performance of various methods of speaker recognition, and a comparision of the performance of automatic methods with that of human listeners. Both text-dependent as well as text-independent speaker-recognition techniques are discussed.
420 citations
01 Jan 1988
405 citations
TL;DR: The performance trade-off of missed detections and false alarms for each system and the effects on performance of training condition, test segment duration, the speakers' sex and the match or mismatch of training and test handsets are presented.
403 citations
TL;DR: The idea is introduced of a "super-wavelet," a linear combination of wavelets that itself is treated as a wavelet that allows the shape of the wavelet to adapt to a particular problem, which goes beyond adapting parameters of a fixed-shape wavelet.
Abstract: Methods are presented for adaptively generating wavelet templates for signal representation and classification using neural networks. Different network structures and energy functions are necessary and are given for representation and classification. The idea is introduced of a "super-wavelet," a linear combination of wavelets that itself is treated as a wavelet. The super-wavelet allows the shape of the wavelet to adapt to a particular problem, which goes beyond adapting parameters of a fixed-shape wavelet. Simulations are given for 1-D signals, with the concepts extendable to imagery. Ideas are discussed for applying the concepts in the paper to phoneme and speaker recognition.
389 citations
TL;DR: A class of linear transformation techniques based on block wise transformation of MFLE which effectively decorrelate the filter bank log energies and also capture speech information in an efficient manner are studied.
389 citations