The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method...

We explore a new spectral representation of speech signals through group delay functions. The group delay functions by themselves are noisy and difficult to interpret owing to zeroes that are close to the unit circle in the z-domain and these clutter the spectra. A new modified group delay function [1] that reduces the effects of zeroes close to the unit circle is used. Assuming that this new f...

Speech signal contains several levels of information. At first it contains information about the spoken message. At second level speech signal also gives information about the speaker identity, his emotional state and so on. The task of speaker recognition can be divided into two parts: speaker identification and speaker verification. Speaker identification is answering the question which one o...

We examined variants of MFCC and PLP cepstral parameterisations in the context of large vocabulary continuous speech recognition under di erent acoustical environmental conditions: Compared to MFCC, mel-frequency PLP uses a cubic root intensity-toloudness law, and an LPC analysis is applied to the mel-warped spectrum. In LPC-smoothed MFCC, the only di erence to MFCC is the additional LPC smooth...

A speech-music classification method according to a developed neural system and beat spectrum is proposed achieve accurate of through pre-emphasis, endpoint detection, framing, windowing other steps preprocess collect vocal music signals. After fast Fourier transforms triangle filter processing, the Mel frequency cepstrum coefficient (MFCC) obtained, discrete cosine transform performed obtain s...

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...

In this paper we study the performance of the low-variance multi-taper Mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) features in a state-ofthe-art i-vector speaker verification system. The MFCC and PLP features are usually computed from a Hamming-windowed periodogram spectrum estimate. Such a singletapered spectrum estimate has large variance, which can be red...

Leak detection techniques based on Machine Learning (ML) models can assist or even replace manual work in leak operations water distribution systems (WDSs). However, studies leakage on-site signals are limited compared to lab-scale detection. The have stronger interference and randomness, while the laboratory relatively simpler. To better operations, present paper develops compares three ML-bas...

In automatic speech recognition mel-frequency cepstral coefficients (MFCC) or linear predictive cepstral coefficients (LPCC) are features commonly used today. However, their calculation considers only a few features of the auditory system. On the assumption that the human representation of speech is an optimal representation, considering more features of the auditory system might lead to a bett...