In this paper we describe and evaluate a series of new algorithms that compensate for the effects of unknown acoustical environments (or changes in environment) through the use of compensation vectors that are added to the cepstral representations of speech that is input to a speech recognition system. These compensation vectors are obtained from direct frame-by-frame comparisons of the cepstra...

This article evaluates the performance of Extreme Learning Machine (ELM) and Gaussian Mixture Model (GMM) in the context of text independent Multi lingual speaker identification for recorded and synthesized speeches. The type and number of filters in the filter bank, number of samples in each frame of the speech signal and fusion of model scores play a vital role in speaker identification accur...

The aim of this work is to increase intelligibility of HMMbased synthetic speech in noisy environments by modifying clean synthetic speech given that noise is known. For that purpose we need a measure for intelligibility of speech in noise that can automatically define the sort of modifications that we need to apply. In previous experiments [1] we have observed that spectrum envelope modificati...

Since their inaugural releases in 2007, Google’s Android and Apple’s iOS have grown to dominate the mobile OS market share. Currently, they jointly possess over 99% of global share with being leading Operating System choice worldwide, controlling close 70% Mobile devices enabled exponential growth a plethora applications that play key roles enabling many use cases are pivotal our daily lives. O...

In a distant-talking environment, the length of channel impulse response is longer than the short-term spectral analysis window. Conventional short-term spectrum based Cepstral Mean Normalization (CMN) is therefore, not effective under these conditions. In this paper, we propose a robust speech recognition method by combining a short-term spectrum based CMN with a long-term one. We assume that ...