Mismatch between training and testing data is a major error source for both Automatic Speech Recognition (ASR) and Automatic Speaker Identification (ASI). In this paper, we first ...
Xi Zhou, Yun Fu, Ming Liu, Mark Hasegawa-Johnson, ...
In conventional speaker recognition methods based on MFCC, phase information has been ignored. We proposed a method that integrated the phase information with MFCC on a speaker id...
Abstract. Weighted distance measure and discriminative training are two different approaches to enhance VQ-based solutions for speaker identification. To account for varying import...
Speaker recognition remains a challenging task under noisy conditions. Inspired by auditory perception, computational auditory scene analysis (CASA) typically segregates speech by...
Robustness is one of the most important topics for automatic speech recognition (ASR) in practical applications. Monaural speech separation based on computational auditory scene a...