Estimation of Formant Frequencies By Means of a Wavelet Transform of the Speech Spectrum

The objective is to present a method that extracts the spectral envelope of a speech signal. The method is based on the wavelet transform, which is a family of multi-resolution analysis methods. The extraction is founded on the observation that spectral envelope and source-related spectral components vary on different frequency scales. The difference between cepstral and wavelet analysis is that the latter is not based on the hypothesis that a speech signal is the outcome of a convolution of the source signal with the vocal tract impulse response. The method of analysis was tested on a corpus of [a],[i],[u] vowels sustained by healthy and dysphonic speakers. Results show that the envelopes extracted via cepstral and wavelet analysis are very similar except in the case of [u], for which the first two formants extracted by means of wavelet analysis are shifted slightly towards higher values and the formant peaks are somewhat closer. Keywords--- Signal Analysis, Cepstrum, Wavelet transfo..

Estimation By Means of Wavelet Analysis of the Signal-to-Noise Ratio of Dysphonic Voices

The objective is to explore a procedure for isolating additive noise in vowels, and estimating the signal-to-noise ratio. The method is founded on the wavelet transform, which is a family of multi-resolution analysis methods. The breakdown of sonorants into a noise and a signal constituent relates to the observation that additive noise is mostly high-frequency or low-amplitude. The segregation of the noise is therefore based on the isolation of the wavelets of the highestresolution scale or, alternatively, on those whose amplitude is below a critical threshold. The analysis method was tested on a corpus of [a],[i],[u] vowels sustained by healthy and dysphonic speakers. Results suggest that isolating noise via thresholding is preferable to assigning high-resolution scales to noise and low-resolution scales to the signal. Keywords--- Signal-to-noise ratio, Wavelet Transform. I. Introduction The article is concerned with gauging the amount of noise in vowels in view of the description ..