Two methods of spectral analysis for noisy speech recognition are proposed and tested in a speaker independent word recognition experiment under an additive white Gaussian noise environment. One is Mel-frequency cepstral coefficients (MFCC) spectral analysis on the autocorrelation sequence of the speech signal and the other is MFCC spectral analysis on its double autocorrelation sequence. The w...

Most conventional features used in speaker recognition are based on spectral envelope characterizations such as Mel-scale filterbank cepstrum coefficients (MFCC), Linear Prediction Cepstrum Coefficient (LPCC) and Perceptual Linear Prediction (PLP). The MFCC’s success has seen it become a de facto standard feature for speaker recognition. Alternative features, that convey information other than ...

Speaker identification for the speech signal processing request, determining speaker is a challenge due to physical variation. This paper emphasizes new algorithm based on acoustic feature analysis of text-dependent speech. In this proposed method changed by ten variation methods. Acoustic all types voice calculated its arithmetical correlation coefficients and mean value. The audio characteris...

This article evaluates the performance of Extreme Learning Machine (ELM) and Gaussian Mixture Model (GMM) in the context of text independent Multi lingual speaker identification for recorded and synthesized speeches. The type and number of filters in the filter bank, number of samples in each frame of the speech signal and fusion of model scores play a vital role in speaker identification accur...

Spectro-temporal Gabor features based on auditory knowledge have improved word accuracy for automatic speech recognition in the presence of noise. In previous work, we generated robust spectro-temporal features that incorporated the power normalized cepstral coefficient (PNCC) algorithm. The corresponding power normalized spectrum (PNS) is then processed by many Gabor filters, yielding a high d...

Problem statement: Fusion weight tuning based on score reliability is imperative in order to ensure the performances of multibiometric systems are sustained. Approach: In this study, two variant of conditions i.e., different performances of individual subsystems and inconsistent quality of test samples are experimented to multibiometric systems. By applying multialgorithm scheme, two types of f...

This paper presents a quantitative metric to describe the multidimensionality of spectral envelope perception, that is, the perception specifically related to the spectral element of timbre. Mel-cepstrum (Mel-frequency cepstral coefficients or MFCCs) is chosen as a hypothetical metric for spectral envelope perception due to its desirable properties of linearity, orthogonality, and multidimensio...

This work presents a noise spectrum estimator based on the Gaussian mixture model (GMM)-based speech presence probability (SPP) for robust speech recognition. Estimated noise spectrum is then used to compute a subband a posteriori signal-to-noise ratio (SNR). A sigmoid shape weighting rule is formed based on this subband a posteriori SNR to enhance the speech spectrum in the auditory domain, wh...

Currently, one of the most widely used distance measures in speech and speaker recognition is the Euclidean distance between mel frequency cepstral coefficients (MFCC). MFCCs are based on filter bank algorithm whose filters are equally spaced on a perceptually motivated mel frequency scale. The value of mel cepstral vector, as well as the properties of the corresponding cepstral distance, are d...

In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower orders, while the higher-order autocorrelation coefficients are least affected, this method discards the lower order autocorrelation coefficients and uses...