In this paper, we present a spectro-temporal feature extraction technique using sub-band Hilbert envelopes of relatively long segments of speech signal. Hilbert envelopes of the sub-bands are estimated using Frequency Domain Linear Prediction (FDLP). Spectral features are derived by integrating the sub-band Hilbert envelopes in short-term frames and the temporal features are formed by convertin...

In this paper the procedure for feature vector extraction and the problems, which must be solved, by defining the feature vectors, which contain only the information about the speech signal are described. A new procedure of feature extraction which is based on the frame elimination and frequency spectrum reduction for the noisy part of the speech signal is proposed. For all tests the Slovenian ...

Conventional automatic speech recognition (ASR) often neglects the spectral phase information in its front-end and feature extraction stages. The aim of this paper is to show the impact that enhancement of the noisy spectral phase has on ASR accuracy when dealing with speech signals corrupted with additive noise. Apart from proof-of-concept experiments using clean spectral phase, we also presen...

This paper describes a robust feature extraction method for continuous speech recognition. Central to the method is the Minimum Variance Distortionless Response (MVDR) method of spectrum estimation and a feature trajectory smoothing technique for reducing the variance in the feature vectors. The above method, when evaluated on continuous speech recognition tasks in a stationary and moving car, ...

Deep neural networks (DNNs) have been successfully applied to a variety of automatic speech recognition (ASR) tasks, both in discriminative feature extraction and hybrid acoustic modeling scenarios. The development of improved loss functions and regularization approaches have resulted in consistent reductions in ASR word error rates (WERs). This paper presents a manifold learning based regulari...

spike sorting is a class of techniques used in the analysis of electrophysiological data. studying the dynamics of neural activity via electrical recording relies on the ability to detect and sort neural spikes recorded from a number of neurons by the same electrode. this article reviews methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting.

Automatic Accents Identification is very important for discussion especially within scope of speaker recognition. Some contribution of appropriate techniques uses in Music Recognition and Accent Identification may contributes in improving the recognition rate. Techniques in discussing on music genre identification or accents automatic identification and the combination of both processes still i...

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 study performs feature extraction for isolated word recognition using Mel-Frequency Cepstral Coefficient (MFCC) for Gujarati language. It explains feature extraction methods MFCC and Linear Predictive Coding (LPC) in brief. The paper compares the performances of MFCC and LPC features under Vector Quantization (VQ) method. The dataset comprising of males and females voices were trained and t...

The majority of speech recognition systems today commonly use Hidden Markov Models (HMMs) as acoustic models in systems since they can powerfully train and map a speech utterance into a sequence of units. Such systems perform even better if the units are context-dependent. Analogously, when HMM techniques are applied to the problem of articulatory feature extraction, contextdependent articulato...