Log area ratio coefficients (LAR) derived from linear prediction coefficients (LPC) is a well known feature extraction technique used in speech applications. This paper presents a novel way to use the LAR feature in a speaker identification system. Here, instead of using the mel frequency cepstral coefficients (MFCC), the LAR feature is used in a Gaussian mixture model (GMM) based speaker ident...

This paper evaluates the impact of low-level features on speaker verification performance, with an emphasis on the recently proposed MFCC variant based on asymmetric tapers (MFCC asymmetric from now on) standalone as features or followed by PCA as linear projection technique applied before the GMM-UBM back-end classifier in clean and noisy environments. The performances of the MFCC-asymmetric f...

Eigen-MLLR coe cients are proposed as new feature parameters for speaker-identi cation in this paper. By performing principle component analysis on MLLR parameters among training speakers, the eigen-MLLR coe cients (EMCs) are derived as the coe cients for the eigenvectors. The discriminating function of the new EMC features based on the Fisher criterion is found to be ten times larger than that...

This paper is concerned with the development of Back-propagation Neural Network for Bangla Speech Recognition. In this paper, ten bangla digits were recorded from ten speakers and have been recognized. The features of these speech digits were extracted by the method of Mel Frequency Cepstral Coefficient (MFCC) analysis. The mfcc features of five speakers were used to train the network with Back...

This paper briefly describes the language recognition system developed by the Sofware Technology Working Group (http://gtts.ehu.es) at the University of the Basque Country in collaboration with IKERLAN Technological Research Center, and submitted to the NIST 2009 Language Recognition Evaluation. The system consists of a hierarchical fusion of individual subsystems: two acoustic GLDS-SVM systems...

Environmental robustness is an important area of research in speech recognition. Mismatch between trained speech models and actual speech to be recognized is due to factors like background noise. It can cause severe degradation in the accuracy of recognizers which are based on commonly used features like mel-frequency cepstral co-efficient (MFCC) and linear predictive coding (LPC). It is well u...

This paper presents the Automatic Genre Classification of Indian Tamil Music andWestern Music using Timbral and Fractional Fourier Transform (FrFT) based Mel Frequency Cepstral Coefficient (MFCC) features. The classifier model for the proposed system has been built using K-NN (K-Nearest Neighbours) and Support Vector Machine (SVM). In this work, the performance of various features extracted fro...

Abstract--The main objective of this research is to develop a speech emotion recognition system using residual phase and MFCC features with autoassociative neural network (AANN). The speech emotion recognition system classifies the speech emotion into predefined categories such as anger, fear, happy, neutral or sad. The proposed technique for speech emotion recognition (SER) has two phases : Fe...

In this paper, improvement of an ASR system for Hindi language, based on Vector quantized MFCC as feature vectors and HMM as classifier, is discussed. MFCC features are usually pre-processed before being used for recognition. One of these pre-processing is to create delta and delta-delta coefficients and append them to MFCC to create feature vector. This paper focuses on all digits in Hindi (Ze...