Missing Feature Theory (MFT), a powerful systematic framework for robust speech recognition, to date has not been optimally applied to linear-transform based features like MFCC or HLDA, which are necessary for state-of-the-art recognition accuracy, due to the intractable multivariate integral in bounded marginalization. This paper seeks to enable more optimal use of MFT with MFCC features/diago...

The aim of this work is to reconstruct clean speech solely from a stream of noise-contaminated MFCC vectors, as may be encountered in distributed speech recognition systems. Speech reconstruction is performed using the ETSI Aurora back-end speech reconstruction standard which requires MFCC vectors, fundamental frequency and voicing information. In this work, fundamental frequency and voicing ar...

The Mel-frequency cepstral coefficients (MFCC) are most widely used and successful features for speech recognition. But, their performance degrades in presence of additive noise. In this paper, we propose a noise compensation method for Mel filter bank energies and so MFCC features. This compensation method includes two steps: Mel sub-band spectral subtraction and then compression of Mel-Sub-ba...

This paper presents a method that combines the techniques of temporal trajectory filtering and projection measure for robust speaker identification. The proposed robust feature, called Relative Autocorrelation Sequence Mel-scale Frequency Cepstral Coefficients (RAS-MFCC), is derived based on filtering the temporal trajectories of short-time one-sided autocorrelation sequences. This filtering pr...

This thesis is concerned with reconstructing an intelligible time-domain speech signal from speech recognition features, such as Mel-frequency cepstral coefficients (MFCCs), in a distributed speech recognition(DSR) environment. The initial reconstruction methods in this thesis require, in addition to MFCC vectors, fundamental frequency and voicing information. In the later parts of the thesis t...

We propose a system to real environmental noise and channel mismatch for forensic speaker verification systems. This method is based on suppressing various types of real environmental noise by using independent component analysis (ICA) algorithm. The enhanced speech signal is applied to mel frequency cepstral coefficients (MFCC) or MFCC feature warping to extract the essential characteristics o...

Eigen-MLLR coe cients are proposed as new feature parameters for speaker-identi cation in this paper. By performing principle component analysis on MLLR parameters among training speakers, the eigen-MLLR coe cients (EMCs) are derived as the coe cients for the eigenvectors. The discriminating function of the new EMC features based on the Fisher criterion is found to be ten times larger than that...

According to the nonlinear characteristic of the speech signal, this paper presents a novel robust MFCC extraction method using sample-ISOMAP. ISOMAP is a nonlinear dimensionality reduction method based on the theory of manifold, it can reveal the meaningful low-dimensional structure hidden in the high-dimensional observations. In the proposed method, ISOMAP is first applied for calculating the...

In a microphone array system, feature combination in the MFCC domain can improve speech recognition accuracy. Multiple microphones provide different feature parameters such as MFCCs even if they have similar speech and noise signals, because of the phase difference and transmission characteristics. In this paper, we investigate how the recognition performance changes when we average multiple MF...