This study presents a compact L-points discrete cosine transform (DCT) hardware accelerator for M-points Mel-scale Frequency Cepstral Coefficients (MFCC). The main contributions of this work can be summarized as 1) proposing an algorithm with lower complexity; 2) achieving higher accuracy performance; 3) implementing low-cost unique group coefficients. For derivation, the proposed...

This paper proposes a new approach to extraction of a corpus-based database of residual signal segments that are used as excitations of a production model [1, 2] to replay MFCC encoded speech signal with natural sound. Neither extra information besides the MFCCs (like F0, voiced/unvoiced flag etc.) nor modification and/or extension of a MFCC computation algorithm is needed. The MFCC algorithm i...

The significance of features derived from complex analytic domain representation of speech, for different applications, is investigated. Frequency domain linear prediction (FDLP) coefficients are derived from analytic magnitude and instantaneous frequency (IF) coefficients are derived from analytic phase of speech signals. Minimal pair ABX (MP-ABX) tasks are used to analyse different features a...

In this work, we validate the effectiveness of our recently proposed integrated template matching and statistical modeling approach on four baseline systems with increasing phone recognition accuracies in the range of 73% to 78% for the TIMIT task. The four baselines were generated using the methods of 1) Discriminative Training (DT) of Minimum Phone Error (MPE), 2) MFCC concatenated with ensem...

Speaker verification and identification systems most often employ HMMs and GMMs as recognition engines. This paper describes an algorithm for the optimal selection of the feature space, suitable for these engines. In verification systems, each speaker (target) is assigned an “individual” optimal feature space in which he/she is best discriminated against impostors. Several feature selection pro...

Using TI digits recognition experiments, we show that a combination of two dynamic speech features, Liftered Forward Masked (LFM) MFCC and 2-D cepstrum, can improve system robustness to additive Volvo noise while maintaining system performance comparable to standard MFCC features in clean conditions. Through experiments, we show that the information extracted by forward masking and by the 2D ce...

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...