http://cs.uef.fi/sipu/pub/SRE-review-Kinnunen-and-Li.pdf

An overview of text-independent speaker recognition: From features to supervectors

A new method for analysis of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) and Fourier-Bessel (FB) expansion has been presented in this paper. The EMD decomposes an EEG signal into a finite set of band-limited signals termed intrinsic mode functions (IMFs). The mean frequency (MF) for each IMF has been computed using FB expansion. The MF measure of the IMFs has been used as a feature in order to identify the difference between ictal and seizure-free intracranial EEG signals. It has been shown that the MF feature of the IMFs has provided statistically significant difference between ictal and seizure-free EEG signals. Simulation results are included to illustrate the effectiveness of the proposed method.

/pdf/discrimination-between-ictal-and-seizure-free-eeg-signals-4t4drug8re.pdf

Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition

Front-end or feature extractor is the first component in an automatic speaker recognition system. Feature extraction transforms the raw speech signal into a compact but effective representation that is more stable and discriminative than the original signal. Since the front-end is the first component in the chain, the quality of the later components (speaker modeling and pattern matching) is strongly determined by the quality of the front-end. In other words, classification can be at most as accurate as the features. Several feature extraction methods have been proposed, and successfully exploited in the speaker recognition task. However, almost exclusively, the methods are adopted directly from the speech recognition task. This is somewhat ironical, considering the opposite nature of the two tasks. In speech recognition, speaker variability is one of the major error sources, whereas in speaker recognition it is the information that we wish to extract. The mel-frequency cepstral coefficients (MFCC) is the most evident example of a feature set that is extensively used in speaker recognition, but originally developed for speech recognition purposes. When MFCC front-end is used in speaker recognition system, one makes an implicit assumption that the human hearing meachanism is the optimal speaker recognizer. However, this has not been confirmed, and in fact opposite results exist. Although several methods adopted from speech recognition have shown to work well in practise, they are often used as “black boxes” with fixed parameters. It is not understood what kind of information the features capture from the speech signal. Understanding the features at some level requires experience from specific areas such as speech physiology, acoustic phonetics, digital signal processing and statistical pattern recognition. According to the author’s general impression of literature, it seems more and more that currently, at the best we are guessing what is the code in the signal that carries our individuality. This thesis has two main purposes. On the one hand, we attempt to see the feature extraction as a whole, starting from understanding the speech production process, what is known about speaker individuality, and then going

http://cs.joensuu.fi/pages/pums/public_results/2004_PhLic_Kinnunen_Tomi.pdf

Spectral Features for Automatic Text-Independent Speaker Recognition

Fourier–Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals

A new technique to reduce cross terms in the Wigner distribution

A compact representation of speech is possible using Bessel functions because of the similarity between voiced speech and the Bessel functions. Both voiced speech and the Bessel functions exhibit quasiperiodicity and decaying amplitude with time. This paper presents the results of speaker identification experiments using features obtained from (1) the Fourier-Bessel expansion and (2) the cepstral representation of speech frames. Identification scores of 65% and 76% were achieved using features based on J/sub 1/(t) expansion of air-to-ground speech transmission databases of 143 and 1054 test utterances, respectively. The corresponding scores for the two databases using cepstral coefficients of a comparable size were 80% and 88%. A comparison of the two sets of features indicates that J/sub 1/(t) can be used to model the hearing perception much like the mel cepstral coefficients.

A comparison of speaker identification results using features based on cepstrum and Fourier-Bessel expansion

The similarity between voiced speech and the Bessel functions yields a compact representation of speech using the nonsinusoidal Bessel functions. This paper presents the results of speaker identification experiments using features obtained from the first order Bessel function expansion of speech frames. Identification results on two large data bases using features based on {J/sub 1/(t)} expansion showed scores ranging from 55% to 75%. The scores depended on the indices of the Bessel functions used in the feature representation.

https://ieeexplore.ieee.org/iel3/4892/13486/00620332.pdf

Speaker identification using features based on first order Bessel function expansion of speech

Speech modification for voice disguising and spectrum modifying applications has been explored using a Fourier-Bessel series (FB) expansion of speech signals. With the approximately bandpass spectrum of the first order Bessel functions, the FB expansion coefficients may be altered and the expansion may be truncated to yield a desired speech quality in the modified speech. Results demonstrate that reasonable quality of altered speech at low bit rate coding can be achieved.

K. Gopalan

Papers

A comparison of speaker identification results using features based on cepstrum and Fourier-Bessel expansion

Speaker identification using features based on first order Bessel function expansion of speech

Speech modification by selective Fourier-Bessel series expansion of speech signals