AHUMADA: A large speech corpus in Spanish for speaker characterization and identification
Speech Communication - Speaker recognition and its commercial and forensic applications
State-of-the-Art Performance in Text-Independent Speaker Verification Through Open-Source Software
IEEE Transactions on Audio, Speech, and Language Processing
Robust Speaker Recognition in Noisy Conditions
IEEE Transactions on Audio, Speech, and Language Processing
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The effect of additive noise in a speaker recognition system is known to be a crucial problem in real life applications. In a speaker recognition system, if the test utterance is corrupted by any type of noise, the performance of the system notoriously degrades. The use of a feature vector selection to determine which speech frames are less affected by noise is the purpose in this work. The selection is implemented using the euclidean distance between the Mel features vectors. Results reflect better performance of robust speaker recognition based on selected feature vector, as opposed to unselected ones, in front of additive noise.