Speaker Recognition by Machines and Humans: A tutorial review

By John H. L. Hansen, T. Hasan. 2015

622 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

VoxCeleb2: Deep Speaker Recognition (2018, neutral): “An in-depth review of these traditional methods is given in [20].”
Icassp 2022 Deep Noise Suppression Challenge (2022, neutral): “There are two motivations to provide clean speech for enrollment of primary talker: (1) speaker models are sensitive to falsealarms in speech activity detection (SAD) [20]; clean speech can be used for obtaining accurate SAD labels which can help improve speaker embeddings.”
The ASVspoof 2017 Challenge: Assessing the Limits of Replay Spoofing Attack Detection (2017, background): “Automatic speaker verification (ASV) [1, 2, 3] technology is used in a growing range of applications which require not only robustness to changes in the acoustic environment, but also resilience to intentional circumvention, known as spoofing [4] or, according to ISO/IEC 30107-1:2016(1) standard, pr”
Conditional Diffusion Probabilistic Model for Speech Enhancement (2022, background): “Speech enhancement, a key element for immersive audio experiences in telecommunication as well as a crucial front-end processor for robust speech recognition [1, 2], assistive hearing [3], and robust speaker recognition [4, 5], is a challenging and still unsolved problem in audio processing.”
Speaker recognition based on deep learning: An overview (2020, methodology): “Maxout DNNs [17] were conceived to properly exploit dropout properties.”
ICASSP 2021 Deep Noise Suppression Challenge (2020, methodology): “activity detection (SAD) [24]; clean speech can be used for obtaining accurate SAD labels.”
Delving into VoxCeleb: environment invariant speaker recognition (2019, methodology): “Traditional literature on speaker recognition [12] have used speaker-dependent GMM mean components as speaker representation features, which are obtained by adapting the UBM to the speaker’s voice.”
Non-intrusive Speech Quality Assessment Using Neural Networks (2019, methodology): “As in the Joint Factor Analysis (JFA) [20], the i-vector framework also considers that speaker and channel variability lies in a lower subspace of the GMM supervectors [21].”