Deconstructing the Cortical Sources of Frequency Following Responses to Speech: A Cross-species Approach

Time-varying pitch is a vital cue in the processing of speech signals. Neural processing of time-varying pitch cues in speech has been extensively assayed using scalp-recorded frequency-following responses (FFRs), which are thought to reflect integrated phase-locked activity from neural ensembles exclusively along the subcortical auditory pathway. Emerging evidence however suggests that the auditory cortex contributes to the FFRs as well. However, the response properties and the relative cortical contribution to the scalp-recorded FFR are only beginning to be explored. Here we used direct intracortical recordings from human subjects and animal models (macaque, guinea pig) to deconstruct the cortical sources of FFRs and leveraged representational similarity analysis as a translational bridge to characterize similarities between the human and animal models. We found robust FFRs in the auditory cortex that emerged from the thalamorecepient layers of the auditory cortex and contributed to the scalp-recorded FFRs via volume conduction.

THE ADVANTAGE OFTHEWAVELET TRANSFORM IN PROCESSING OF SPEECH SIGNALS

This work deals with basic theory of wavelet transform and multi-scale analysis of speech signals, briefly reviewed the main differences between wavelet transform and Fourier transform in the analysis of speech signals. The possibilities to use the method of wavelet analysis to speech recognition systems and its main advantages. In most existing systems of recognition and analysis of speech sound considered as a stream of vectors whose elements are some frequency response. Therefore, the speech processing in real time using sequential algorithms requires computing resources with high performance. Examples of how this method can be used when processing speech signals and build standards for systems of recognition.Key words: digital signal processing, Fourier transform, wavelet analysis, speech signal, wavelet transform

COMPARATIVE CHARACTERISTICS OF SOFTWARE SYSTEMS FOR ANALYSIS AND PROCESSING OF SPEECH SIGNALS USING WAVELETS

Objectives. This article is devoted to the problem of processing and analysis of speech signals on the basis of the wavelet transform method, which has become one of the most relevant in recent years.Method. The growing relevance and undoubted practical value became the reason for the emergence of a large number of software systems that allow the processing of speech signals on the basis of this method. However, each of these systems has significant differences in the interface provided by the processing tools, functions, has a number of advantages and disadvantages. At the moment, a large number of manuals and recommendations for specific software packages have been written, but these materials are fragmented and unsystematic.Result. This article attempts to systematize the theoretical material and describe the similarities and differences, advantages and disadvantages of the three most popular software systems: 1) MATLAB 6.0/6.1/6.5 Wavelet Toolbox 2/2.1/2.2; 2) Mathcad; 3) Wavelet Explorer of Mathematica.Conclusion. This article will be useful for specialists dealing with the problem of speech signal processing using the wavelet transform method, as it contains material that has practical value, and will allow to facilitate the work of a specialist related to the selection of the optimal for the implementation of a specific task of the software complex.

Classificação de disfonias por meio da análise de medidas não lineares e de quantificação de recorrência

In recent years techniques of digital processing of speech signals have been used as an auxiliary tool in the evaluation of vocal deviations, providing the patient with greater comfort low cost and objectivity when compared to the techniques traditionally employed, such as perceptual-auditory analysis. The evaluation of vocal quality, through acoustic analysis of voice signals, is becoming a very popular clinical practice for the detection of vocal disorders that in some cases can be caused by laryngeal lesions or vocal abuse. In this research, we used some traditional non-linear measures combined with measures of recurrence quantification for the discriminative analysis of vocal deviations, breathiness, roughness and strain. The characteristics of the non-linear dynamic analysis,used in the classification process, were the Reconstruction Step (τ), the First Minimum of the Mutual Information Function (PM) and the Correlation Dimension (D2). The quantification measures employed were: Determinism (Det), Shannon entropy (Entr), Mean length of diagonal lines (Lmed), Maximum length of vertical lines (Vmax) and Transitivity (Trans). Through these statistical tests, the potential of each characteristic to discriminate the types of voice signals was evaluated. In the classification process, the neural network MLP (Multilayer Perceptron) was used, with supervised learning algorithm Graded Conjugate Gradient (SCG). There was an average accuracy of 90% in the discrimination between healthy and deviant voices. In the classification between healthy and strained voices, an average accuracy of 76% was obtained with the combined measures Trans, τ , Vmax, Lmed, Det and D2. In the detection of the roughness deviation, an average accuracy of 89% was obtained with the Lmed, Entr, Trans and D2 measures and in the distinction between healthy and breathy voices, 91.17% of accuracy was obtained with only two combined measures, Trans and τ , showing the promising character of the used technique.