Phoneme is the smallest contrastive unit in the sound system of a language. Moreover, it has a meaningful role in speech recognition. In this study, we are interesting for phonemes recognition of Timit database using HTK toolkit for HMM. The main goal is to determine the optimal parameters for the recognizer. For this reason, different speech analysis techniques were operated such as Mel Freque...

This work presents a novel approach for the automatic detection of pathological voices based on fusing the information extracted by means of mel-frequency cepstral coefficients (MFCC) and features derived from the modulation spectra (MS). The system proposed uses a two-stepped classification scheme. First, the MFCC and MS features were used to feed two different and independent classifiers; and...

The current paper proposes skew Gaussian mixture models for speaker recognition and an associated algorithm for its training from experimental data. Speaker identification experiments were conducted, in which speakers were modeled using the familiar Gaussian mixture models (GMM), and the new skewGMM. Each model type was evaluated using two sets of feature vectors, the mel-frequency cepstral coe...

In this paper, we consider the use of multiple acoustic features of the speech signal for continuous speech recognition. A novel articulatory motivated acoustic feature is introduced, namely the spectrum derivative feature. The new feature is tested in combination with the standard Mel Frequency Cepstral Coefficients (MFCC) and the voicedness features. Linear Discriminant Analysis is applied to...

ICA which is generally used for blind source separation problem has been tested for feature extraction in Speech recognition system to replace the phoneme based approach of MFCC. Applying the Cepstral coefficients generated to ICA as preprocessing has developed a new signal processing approach. This gives much better results against MFCC and ICA separately, both for word and speaker recognition...

Similarity measurement is an important part of speaker identification. This study has modified the similarity measurement technique performed in previous studies. Previous studies used the sum of the smallest distance between the input vectors and the codebook vectors of a particular speaker. In this study, the technique has been modified by selecting a particular speaker codebook which has the...

This study proposes a new set of feature parameters based on wavelet packet transform analysis of the speech signal. The new speech features are named subband based cepstral parameters (SBC) and wavelet packet parameters (WPP). The ability of each parameter set to capture speaker identity conveyed in the speech signal is compared to the widely used Mel-frequency cepstral coee-cents (MFCC). The ...

Three different auditory representations—Lyon’s cochlear model, Patterson’s gammatone filterbank combined with Meddis’ inner hair cell model, and mel-frequency cepstral coefficients—are analyzed in connection with self-organizing maps to evaluate their suitability for a perceptually justified classification of sounds. The self-organizing maps are trained with a uniform set of test sounds prepro...

In this paper, the opening work on the development of a Lithuanian HMM speech recognition system is described. The triphone single-Gaussian HMM speech recognition system based on Mel Frequency Cepstral Coefficients (MFCC) was developed using HTK toolkit. Hidden Markov model’s parameters were estimated from phone-level hand-annotated Lithuanian speech corpus. The system was evaluated on a speake...

The procedure of calculating Mel Frequency based Cepstral Coefficients (MFCC) is shown to resemble a three layer Multilayer Perceptron (MLP) like structure. Such an MLP is employed as a preprocessor in a hybrid HMM-MLP system, and the possibility of optimizing the whole system as a single entity, with respect to a suitable criterion, is pointed out. This system, together with the Maximum Mutual...