It is common practice to use similar or even the same feature extraction methods for automatic speech recognition and speaker identification. While the front-end for the former requires to preserve phoneme discrimination and to compensate for speaker differences to some extend, the front-end for the latter has to preserve the unique characteristics of individual speakers. It seems, therefore, c...

In this paper we introduce a robust feature extractor, dubbed as Modified Function Cepstral Coefficients (MODFCC), based on gammachirp filterbank, Relative Spectral (RASTA) and Autoregressive Moving-Average (ARMA) filter. The goal of this work is to improve the robustness of speech recognition systems in additive noise and real-time reverberant environments. In speech recognition systems Mel-Fr...

Several features were compared with regard to recognition performance in a musical instrument recognition system. Both mel-frequency and linear prediction cepstral and delta cepstral coefficients were calculated. Linear prediction analysis was carried out both on a uniform and a warped frequency scale, and reflection coefficients were also used as features. The performance of earlier described ...

The automatic identification of musical instruments is a relatively unexplored and potentially very important field for its promise to free humans from time-consuming searches on the Internet and indexing of audio material. Speaker identification techniques have been used in this paper to determine the properties (features) which are most effective in identifying a statistically significant num...

Three simple speaker verification techniques based on vector quantization, sphericity models and dynamic time warping, respectively, are developed and tested using the same experimental protocol. Two types of feature vectors, the linear prediction derived cepstral coefficients and the melfrequency cepstral coefficients are considered. The efficiency of the combination of the type of acoustic an...

This paper presents an audio genre detection framework that can be used for a multi-language audio corpus. Cepstral coefficients are considered and analyzed as the feature set for both a language dependent and language independent genre identification (GID) task. Language information is found to increase the overall detection accuracy on an average by at least 2.6% from its language independent...

The generalized cepstral analysis method is viewed as a unified approach to the cepstral method and the linear prediction method, in which the model spectrum varies continuously from all-pole to cepstral according to the value of a parameter γ. Since the human ear has high resolution at low frequencies, introducing similar characteristics to the model spectrum, we can represent speech spectrum ...

Lung sounds convey useful information related to pulmonary pathology. In this paper, short-term spectral characteristics of lung sounds are studied to characterize the lung sounds for the identification of associated diseases. Motivated by the success of cepstral features in speech signal classification, we evaluate five different cepstral features to recognize three types of lung sounds: norma...

The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method...

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...