Estimating Formant Frequencies of Vowels Sung by Sopranos Using Weighted Linear Prediction.

This study introduces the weighted linear prediction adapted to high-pitched singing voices (WLP-HPSV) method for accurately estimating formant frequencies of vowels sung by lyric sopranos. The WLP-HPSV method employs a variant of the WLP analysis combined with the zero-frequency filtering (ZFF) technique to address specific challenges in formant estimation from singing signals. Evaluation of the WLP-HPSV method compared to the LPC method demonstrated its superior performance in accurately capturing the spectral characteristics of synthetic /u/ vowels and the /a/ and /u/ natural singing vowels.

Non-stationary Bayesian estimation of parameters from a body cover model of the vocal folds.

The evolution of reduced-order vocal fold models into clinically useful tools for subject-specific diagnosis and treatment hinges upon successfully and accurately representing an individual patient in the modeling framework. This, in turn, requires inference of model parameters from clinical measurements in order to tune a model to the given individual. Bayesian analysis is a powerful tool for estimating model parameter probabilities based upon a set of observed data.

Evolving Spiking Neural Networks for Recognition of Aged Voices.

The aging of the voice, known as presbyphonia, is a natural process that can cause great change in vocal quality of the individual. This is a relevant problem to those people who use their voices professionally, and its early identification can help determine a suitable treatment to avoid its progress or even to eliminate the problem. This work focuses on the development of a new model for the identification of aging voices (independently of their chronological age), using as input attributes parameters extracted from the voice and glottal signals.

Voice Signals Produced With Jitter Through a Stochastic One-mass Mechanical Model.

Background: The quasiperiodic oscillation of the vocal folds causes perturbations in the length of the glottal cycles, which are known as jitter. The observation of the glottal cycles variations suggests that jitter is a random phenomenon described by random deviations of the glottal cycle lengths in relation to a corresponding mean value and, in general, its values are expressed as a percentage of the duration of the glottal pulse.

Objective: The objective of this paper is the construction of a stochastic model for jitter using a one-mass mechanical model of the vocal folds, which assumes complete right-left symmetry of the vocal folds, and which considers motions of the vocal folds only in the horizontal direction.

Analysis and Classification of Voice Pathologies Using Glottal Signal Parameters.

The classification of voice diseases has many applications in health, in diseases treatment, and in the design of new medical equipment for helping doctors in diagnosing pathologies related to the voice. This work uses the parameters of the glottal signal to help the identification of two types of voice disorders related to the pathologies of the vocal folds: nodule and unilateral paralysis. The parameters of the glottal signal are obtained through a known inverse filtering method, and they are used as inputs to an Artificial Neural Network, a Support Vector Machine, and also to a Hidden Markov Model, to obtain the classification, and to compare the results, of the voice signals into three different groups: speakers with nodule in the vocal folds; speakers with unilateral paralysis of the vocal folds; and speakers with normal voices, that is, without nodule or unilateral paralysis present in the vocal folds.

Classification of vocal aging using parameters extracted from the glottal signal.

This article proposes and evaluates a method to classify vocal aging using artificial neural network (ANN) and support vector machine (SVM), using the parameters extracted from the speech signal as inputs. For each recorded speech, from a corpus of male and female speakers of different ages, the corresponding glottal signal is obtained using an inverse filtering algorithm. The Mel Frequency Cepstrum Coefficients (MFCC) also extracted from the voice signal and the features extracted from the glottal signal are supplied to an ANN and an SVM with a previous selection.