The conventional view on the problem of robustness in speech recognition is that performance degradation in ASR systems is due to mismatch between training and test conditions. If problem of robustness in ASR systems were considered as a mismatch between the training and testing conditions the solution would be to find a way to reduce it. Common approaches are: Data-Driven methods such as speec...

The main aim of this experiment is to separate the excitation of vocal tract components of the given speech signal by cepstral analysis. The first step is to convert the speech into short-term segments of size 15-20 ms. Then multiplication of each frame is carried out with the help of Hamming Window. The representation of short term speech in cepstral analysis is computed by finding the IDFT of...

The identiication of aspects of cepstral features that contain a high degree of speaker speciicity potentially can simplify the task of speaker recognition. The identiication process can be performed both temporally and cepstrally. The temporal analysis determines which phonemes or utterances exhibit the highest degree of speaker speciicity, while the cepstral analysis examines individual cepst...

Missing data theory has been applied to the problem of speech recognition in adverse environments. The resulting systems require acoustic models that are expressed in the spectral rather than in the cepstral domain, which leads to loss of accuracy. Cepstral Missing Data Techniques (CMDT) surmount this disadvantage, but require significantly more computation. In this paper, we study alternatives...

In this paper, we describe a prototype speaker identification system using auto-associative neural network (AANN) and formant features. Our experiments demonstrate that formants extracted from difference spectrum perform significantly better than formants extracted from normal spectrum for the task of speaker identification. We also demonstrate that formants from difference spectrum provide com...

This paper presents experimental results on whispered speech recognition based Teager Energy Operator for linear and mel cepstral coefficients including the Cepstral Mean Subtraction normalization technique. The feature vectors taken into consideration are Linear Frequency Coefficients, Mel Coefficients Coefficients. A speaker dependent scenario is used. For process, Dynamic Time Warping Hidden...

This paper addresses the performance of various statistical data fusion techniques for combining the complementary score information in speaker verification. The complementary verification scores are based on the static and delta cepstral features. Both LPCC (Linear prediction-based cepstral coefficients) and MFCC (mel-frequency cepstral coefficients) are considered in the study. The experiment...

Shifted delta cepstral (SDC) features, obtained by concatenating delta cepstral features across multiples speech frames, were recently reported to produce superior performance to delta cepstral features in language and speaker recognition systems. In this paper, the use of SDC features in a speaker verification experiment is reported. Mutual information between SDC features and identity of a sp...

We studied application of the cepstral analyses to the real-time signal processing. The paper describes the utilization and scope of the signal exponential weighting, diierent approaches to the phase unwrapping, and their computational complexity. A new unwrapping method that reduces the cepstral aliasing by combining the real and the diierential cepstrum is also presented. Its complexity is O(...

In this article a new flexible speech detection method comprising two relatively modern approaches like artificial neural networks (ANN) and cepstral matrices is presented. Cepstral matrices obtained via linear prediction coefficients were chosen as the eligible speech features. This technique is known to provide reliable log spectrum estimation at a low cost. Furthermore, both spectral and tim...