Root-cepstral analysis has been proposed previously for speech recognition in car environments [9]. In this paper, we focus on an alternative aspect of Root-cepstrum as it applies to discriminative acoustic modeling and fast speech recognizer decoding. We compare Root-cepstrum to Mel-Frequency cepstrum Coefficients (MFCC) in terms of their noise immunity during modeling and decoding speed. Our ...

Most conventional features used in speaker authentication are based on estimation of spectral envelopes in one way or another, e.g., Mel-scale Filterbank Cepstrum Coefficients (MFCCs), Linear-scale Filterbank Cepstrum Coefficients (LFCCs) and Relative Spectral Perceptual Linear Prediction (RASTA-PLP). In this study, Spectral Subband Centroids (SSCs) are examined. These features are the centroid...

Three different methodologies for automatic speaker identification have been evaluated in the paper, namely the well known Dynamic Time Warping (DTW), the Auto-Regressive Vector Models (ARVM) and an Algebraic Approach (AA). The aim of our study is to examine the effectiveness of these approaches in the fixed-text speaker identification task with short phrases in Bulgarian language collected ove...

In monaural blind audio source separation scenarios, a signal mixture is usually separated into more signals than active sources. Therefore it is necessary to group the separated signals to the final source estimations. Traditionally grouping methods are supervised and thus need a learning step on appropriate training data. In contrast, we discuss unsupervised clustering of the separated channe...

Currently, one of the most widely used distance measures in speech and speaker recognition is the Euclidean distance between mel frequency cepstral coefficients (MFCC). MFCCs are based on filter bank algorithm whose filters are equally spaced on a perceptually motivated mel frequency scale. The value of mel cepstral vector, as well as the properties of the corresponding cepstral distance, are d...

The Mel-Frequency Cepstrum Coefficients (MFCC) is a widely used set of feature used in automatic speech recognition systems introduced in 1980 by Davis and Mermelstein [2]. In this traditional implementation, the 0 coefficient is excluded for the reason it is somewhat unreliable. In this paper, we analyze this term and find that it can be regarded as the generalized frequency band energy (FBE) ...

Our challenge is to analyze/classify video sound track content for indexing purposes. To this end we compare the performance of MPEG-7 Audio Spectrum Projection (ASP) features based on several basis decomposition algorithms vs. Mel-scale Frequency Cepstrum Coefficients (MFCC). For basis decomposition in the feature extraction we evaluate three approaches: Principal Component Analysis (PCA), Ind...

We present a comparison of 6 methods for classification of sports audio. For the feature extraction we have two choices: MPEG-7 audio features and Mel-scale Frequency Cepstrum Coefficients (MFCC). For the classificaiton we also have two choices: Maximum Likelihood Hidden Markov Models (ML-HMM) and Entropic Prior HMM(EP-HMM). EP-HMM, in turn, have two variations: with and without trimming of the...

In this paper, dimension-reduced, decorrelated spectral features for general sound recognition are applied to segment conversational speech of both broadcast news audio and panel discussion television programs. Without a priori information about number of speakers, the audio stream is segmented by a hybrid metric-based and model-based segmentation algorithm. For the measure of the performance w...

Abstract Aiming at the issue that recognition accuracy of traditional acoustic signal features is low for helicopter signals with wind noise in near field, a method extracting mixed MFCC+GFCC based on wavelet decomposition proposed. Firstly, three-layer and reconstruction are applied to signals; then, Mel-Frequency Cepstral Coefficients (MFCC) Gammatone-Frequency Cepstrum Coefficient (GFCC) res...