Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Using mel-frequency cepstral coefficients in missing data technique
EURASIP Journal on Applied Signal Processing
Live speaker identification in conversations
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A novel framework for efficient automated singer identification in large music databases
ACM Transactions on Information Systems (TOIS)
New robust subband Cepstral feature for isolated world recognition
Proceedings of the International Conference on Advances in Computing, Communication and Control
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Proceedings of the 7th international conference on Mobile systems, applications, and services
Robust speech recognition using evolutionary class-dependent LDA
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Robust Speaker Recognition in Noisy Conditions
IEEE Transactions on Audio, Speech, and Language Processing
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Manifestation of noise-in-speech is inherent unless a conscious effort is made to minimize the disturbances in the surroundings while recording speech. The performance of a speech recognition system often degrades in the presence of noise. In this paper, we study the effect of noise in the speech signal on the extracted speech features that are used in speech recognition. Mel frequency cepstral coefficients (MFCCs) are the most popularly used speech features in speech and speaker recognition applications. We first show theoretically, how additive Gaussian noise with mean, µ and variance, σ2 effects the speech parameters (MFCCs). The mean and variance of the error in MFCC due to noise-in-speech is related to the mean and variance of the noise added. We experimentally verify that additive Gaussian noise-in-speech results in an error in MFCC parameter estimation which is also Gaussian.