Journal ArticleDOI
Mean Hilbert envelope coefficients (MHEC) for robust speaker and language identification
TLDR
Experimental results indicate that: (i) the MHEC feature is highly effective and performs favorably compared to other conventional and state-of-the-art front-ends, and (ii) the power-law non-linearity consistently yields the best performance across different conditions for both SID and LID tasks.About:
This article is published in Speech Communication.The article was published on 2015-09-01. It has received 61 citations till now. The article focuses on the topics: Feature extraction & Feature (computer vision).read more
Citations
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Journal ArticleDOI
Advances in phase-aware signal processing in speech communication
TL;DR: It is shown that phase-aware signal processing is an important emerging field with high potential in the current speech communication applications and can complement the possible solutions that magnitude-only methods suggest.
Journal ArticleDOI
Speaker identification features extraction methods: A systematic review
TL;DR: It is identified that the current SI research trend is to develop a robust universal SI framework to address the important problems of SI such as adaptability, complexity, multi-lingual recognition, and noise robustness.
Journal ArticleDOI
Spoofing detection goes noisy
TL;DR: A significant gap is revealed between the performance of state-of-the-art spoofing detectors between clean and noisy conditions and a study with two score fusion strategies shows that combining different feature based systems improves recognition accuracy for known and unknown attacks in both clean and noise conditions.
Journal ArticleDOI
Local spectral variability features for speaker verification
Sahidullah,Tomi Kinnunen +1 more
TL;DR: Combining local covariance information with the traditional cepstral features holds promise as an additional speaker cue in both text-independent and text-dependent recognition.
Journal ArticleDOI
Curriculum Learning Based Approaches for Noise Robust Speaker Recognition
Shivesh Ranjan,John H. L. Hansen +1 more
TL;DR: This study introduces a novel class of curriculum learning (CL) based algorithms for noise robust speaker recognition at two stages within a state-of-the-art speaker verification system: at the i-Vector extractor estimation and at the probabilistic linear discriminant (PLDA) back-end.
References
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Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Journal ArticleDOI
Suppression of acoustic noise in speech using spectral subtraction
TL;DR: A stand-alone noise suppression algorithm that resynthesizes a speech waveform and can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.
Journal ArticleDOI
Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
S. Davis,Paul Mermelstein +1 more
TL;DR: In this article, several parametric representations of the acoustic signal were compared with regard to word recognition performance in a syllable-oriented continuous speech recognition system, and the emphasis was on the ability to retain phonetically significant acoustic information in the face of syntactic and duration variations.
Journal ArticleDOI
Front-End Factor Analysis for Speaker Verification
TL;DR: An extension of the previous work which proposes a new speaker representation for speaker verification, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis, named the total variability space because it models both speaker and channel variabilities.
Journal ArticleDOI
Perceptual linear predictive (PLP) analysis of speech
TL;DR: A new technique for the analysis of speech, the perceptual linear predictive (PLP) technique, which uses three concepts from the psychophysics of hearing to derive an estimate of the auditory spectrum, and yields a low-dimensional representation of speech.
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