In this paper, we investigate the use of block quantisers based on Gaussian mixture models (GMMs) for the coding of Mel frequency-warped cepstral coefficient (MFCC) features in distributed speech recognition (DSR) applications. Specifically, we consider the multi-frame scheme, where temporal correlation across MFCC frames is exploited by the Karhunen–Loève transform of the block quantiser. Comp...

To create systems that understand the sounds that humans are exposed to in everyday life, we need to represent sounds with features that can discriminate among many different sound classes. Here, we use a sound-ranking framework to quantitatively evaluate such representations in a large-scale task. We have adapted a machine-vision method, the passive-aggressive model for image retrieval (PAMIR)...

An efficient speech to text converter for mobile application is presented in this work. The prime motive is to formulate a system which would give optimum performance in terms of complexity, accuracy, delay and memory requirements for mobile environment. The speech to text converter consists of two stages namely front-end analysis and patte rn recognition. The front end analysis involves prepro...

Vocal Tract Length Normalization (VTLN) for standard filterbank-based Mel Frequency Cepstral Coefficient (MFCC) features is usually implemented by warping the center frequencies of the Mel filterbank, and the warping factor is estimated using the maximum likelihood score (MLS) criterion (Lee and Rose, 1998). A linear transform (LT) equivalent for frequency warping (FW) would enable more efficie...

This thesis is concerned with reconstructing an intelligible time-domain speech signal from speech recognition features, such as Mel-frequency cepstral coefficients (MFCCs), in a distributed speech recognition(DSR) environment. The initial reconstruction methods in this thesis require, in addition to MFCC vectors, fundamental frequency and voicing information. In the later parts of the thesis t...

We propose a system to real environmental noise and channel mismatch for forensic speaker verification systems. This method is based on suppressing various types of real environmental noise by using independent component analysis (ICA) algorithm. The enhanced speech signal is applied to mel frequency cepstral coefficients (MFCC) or MFCC feature warping to extract the essential characteristics o...

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...

According to the nonlinear characteristic of the speech signal, this paper presents a novel robust MFCC extraction method using sample-ISOMAP. ISOMAP is a nonlinear dimensionality reduction method based on the theory of manifold, it can reveal the meaningful low-dimensional structure hidden in the high-dimensional observations. In the proposed method, ISOMAP is first applied for calculating the...

In a microphone array system, feature combination in the MFCC domain can improve speech recognition accuracy. Multiple microphones provide different feature parameters such as MFCCs even if they have similar speech and noise signals, because of the phase difference and transmission characteristics. In this paper, we investigate how the recognition performance changes when we average multiple MF...