In this paper, we propose a transform-based adaptation technique for robust speech recognition in unknown environments. It uses maximum likelihood spectral transform (MLST) algorithm with additive and convolutional noise parameters. Previously many adaptation algorithms have been proposed in the cepstral domain. Though the cepstral domain may be appropriate for the speech recognition, it is dif...

In this paper, we investigate the noise-robustness of features based on the cepstral time coefficients (CTC). By cepstral time coefficients, we mean the coefficients obtained from applying the discrete cosine transform to the commonly used mel-frequency cepstral coefficients (MFCC). Furthermore, we apply temporal filters used for computing delta and acceleration dynamic features to the CTC, res...

Recently, speaker verification systems using different kinds of prosodic features have been proposed. Although it has been shown that most of these speaker verification systems can improve system performance using score-level fusion with stateof-the-art cepstral-based systems, a systematic comparison of the prosodic modelling algorithms used in these prosodic systems has not yet been performed....

In this paper we present oblivious schemes of ghost-based watermarking for DCT coefficients and examine the fundamental resistance to some attacks. First we introduce a bipolar ghost model, which consists of a composite signal and its complementary signal, and formulate the power cepstrum for the model. Next we define the cepstral difference between the composite and complementary signals to im...

Reliability of Automatic Speaker Verification (ASV) systems has always been a concern in dealing with spoofing attacks. Among these attacks, replay attack is the simplest and the easiest accessible method. This paper describes a replay spoofing detection system applied to ASVspoof2017 corpus. To reach this goal, features such as Constant-Q Cepstral Coefficients (CQCC), Modified Group Delay (MGD...

In this paper we have studied two information fusion approaches, namely feature vector concatenation and decision fusion, for the task of reducing error rates in a speaker verification system used in mismatched conditions. Three types of features are fused: Mel Frequency Cepstral Coefficients (MFCC), MFCC with Cepstral Mean Subtraction (CMS) and Maximum Auto-Correlation Values (MACV). We have u...

In this paper, cepstral features derived from the differential power spectrum (DPS) are proposed for improving the robustness of a speech recognizer in presence of background noise. These robust features are computed from the speech signal of a given frame through the following four steps. First, the short-time power spectrum of speech signal is computed from the speech signal through the fast ...

The paper describes the problem of cepstral speech analysis in the process of automated voice disorder probability estimation. The author proposes to derive two of the most diagnostically significant voice features: quality of harmonic structure and degree of subharmonic from cepstrum of speech signal. Traditionally, these attributes are estimated auricularly or by spectrum (or spectrogram) obs...

Tandem systems transform the cepstral features into posterior probabilities of subword units using artificial neural networks (ANNs), which are processed to form input features for conventional speech recognition systems. They have been shown to perform better than conventional speech recognition systems using cepstral features. Recent studies have shown that modelling cepstral features with au...

We present a practical and noise-robust speech recognition system which estimates a target-to-interferers power ratio using a zero-crossing-based binaural model and applies the power ratio to a channel attentive missing feature decoder in the cepstral domain. In a natural multisource environment, our binaural model extracts spatial cues at each zero-crossing of a filterbank output signal to loc...