The aim of this study was to assess the applicability of Mel Frequency Cepstral Coefficients (MFCC) of voice samples in diagnosing vocal nodules and polyps. Patients’ voice samples were analysed acoustically with the measurement of MFCC and values of the first three formants. Classification of mel coefficients was performed by applying the Sammon Mapping and Support Vector Machines. For the tes...

This paper describes a speaker identification system that uses complementary acoustic features derived from the vocal source excitation and the vocal tract system. Conventional speaker recognition systems typically adopt the cepstral coefficients, e.g., Mel-frequency cepstral coefficients (MFCC) and linear predictive cepstral coefficients (LPCC), as the representative features. The cepstral fea...

Power-spectrum-based Mel-Frequency Cepstrum Coefficients (MFCC) is usually used as a feature extractor in a speaker identification system. This one-dimensional feature extraction subsystem, however, shows low recognition rates for identifying utterance speech signals under harsh noise conditions. In this paper, we have developed a speaker identification system based on Bispectrum data that is m...

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...

Automatic Speech Recognition (ASR) involves mainly two steps; feature extraction and classification (pattern recognition). Mel Frequency Cepstral Coefficient (MFCC) is used as one of the prominent feature extraction techniques in ASR. Usually, the set of all 12 MFCC coefficients is used as the feature vector in the classification step. But the question is whether the same or improved classifica...

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...