An overview of automatic speaker recognition technology

By Douglas A. Reynolds. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002

681 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/691ec89cb048ffe78b6e1eaccb0df7abbd44076d

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Citations in the corpus

Speech Recognition Using Deep Neural Networks: A Systematic Review (2019, background): “These cases comprise, but not limited to, district or other government institutions, calls to radio stations, insurance agencies, or documented conversations [15], [16].”
Learning with Augmented Class by Exploiting Unlabeled Data (2014, methodology): “It has been applied in face recognition (Li and Wechsler 2005), speaker recognition (Reynolds 2002), etc.”
An Overview of Speaker Identification: Accuracy and Robustness Issues (2011, background): “The interested reader is referred to [2],[6],[7],[9],[10] for a review of the methods and technologies specifically directed at speaker verification.”
SoundSense: scalable sound sensing for people-centric applications on mobile phones (2009, methodology): “For voice and music input, techniques such as speech recognition [32], speaker identification [33], or music genre classification [41] can be applied.”
A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses (2005, background): “The reason is that the component Gaussians are acting together to model the overall probability density function and the linear combination of diagonal covariance Gaussians is capable of modeling the correlations between feature vector elements [16].”
EmotionSense: a mobile phones based adaptive platform for experimental social psychology research (2010, methodology): “With respect to voice processing technologies, a survey of GMM-based speaker recognition technology can be found in [26].”
Learning Speaker-Specific Characteristics With a Deep Neural Architecture (2011, background): “For the last two decades, systems based on generative modelling of the speakers are generally credited as the most successful in applications like speaker verification, identification and detection [26], [27], [28], [40], [44], [82].”
Speaker recognition based on deep learning: An overview (2020, methodology): “Dropout is a useful strategy to prevent over-fitting in DNN finetuning when using a small training set [18][19].”
EgoCom: A Multi-Person Multi-Modal Egocentric Communications Dataset (2020, methodology): “EgoCom amplifies egocentric audio relative to quieter surrounding audio thereby simplifying tasks like speaker identification [14]: a simple solution is to use themaximummagnitude of aligned audio.”
A New Database for Speaker Recognition (2005, neutral): “Reynolds’ review [12] and T.”