In the presence of noise and sensor mismatch condition performance of a conventional automatic Hindi speech recognizer starts to degrade, while we human being are able to segregate, focus and recognize the target speech. In this paper, we have used auditory based feature extraction procedure Gammatone frequency cepstral coefficient (GFCC) for Hindi phoneme classification. To distinguish vowels ...

Automatic detection of syllable repetition is one of the important parameter in assessing the stuttered speech objectively. The existing method which uses artificial neural network (ANN) requires high levels of agreement as prerequisite before attempting to train and test ANNs to separate fluent and nonfluent. We propose automatic detection method for syllable repetition in read speech for obje...

This paper presents a new approach for classification of dysfluent and fluent speech using Mel-Frequency Cepstral Coefficient (MFCC). The speech is fluent when person's speech flows easily and smoothly. Sounds combine into syllable, syllables mix together into words and words link into sentences with little effort. When someone's speech is dyfluent, it is irregular and does not flow effortlessl...

Heart failure (HF) is a devastating condition that impairs people’s lives and health. Because of the high morbidity mortality associated with HF, early detection becoming increasingly critical. Many studies have focused on field heart disease diagnosis based sound (HS), demonstrating feasibility signals in diagnosis. In this paper, we propose non-invasive method for HF deep learning (DL) networ...

Automatic voice pathology detection enables objective assessment of pathologies that affect the production mechanism. Detection systems have been developed using traditional pipeline approach (consisting feature extraction part and part) modern deep learning -based end-to-end approach. Due to lack vast amounts training data in study area pathological voice, former is still a valid choice. In ex...

Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature ex...

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...