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The Influence of the Amplitude Spectrum Correction in the HFCC Parametrization on the Quality of Speech Signal Frame Classification

Gmyrek Stanislaw, Hossa Robert, Makowski Ryszard

Archives of Acoustics

2025

Vol. 50, nr 1

59--67

The voiced parts of the speech signal are shaped by glottal pulse excitation, the vocal tract, and the speaker’s lips. Semantic information contained in speech is shaped mainly by the vocal tract. Unfortunately, the quasiperiodicity of the glottal excitation, in the case of HFCC parameterization, is one of the factors affecting the significant scatter of the feature vector values by introducing ripples into the amplitude spectrum. This paper proposes a method to reduce the effect of quasiperiodicity of the excitation on the feature vector. For this purpose, blind deconvolution was used to determine the vocal tract transfer function estimator and the corrective function of the amplitude spectrum. Then, on the basis of the obtained HFCC parameters, statistical models of individual Polish speech phonemes were developed in the form of mixtures of Gaussian distributions, and the influence of the correction on the quality of classification of speech frames containing Polish vowels was investigated. The aim of the correction was to narrow the GMM distributions, which, according to detection theory, reduces the classification errors. The results obtained confirm the effectiveness of the proposed method.

Amplitude spectrum correction to improve speech signal classification quality

Gmyrek Stanislaw, Hossa Robert, Makowski Ryszard

International Journal of Electronics and Telecommunications

2024

Vol. 70, No. 3

569-574

The speech signal can be described by three key elements: the excitation signal, the impulse response of the vocal tract, and a system that represents the impact of speech production through human lips. The primary carrier of semantic content in speech is primarily influenced by the characteristics of the vocal tract. Nonetheless, when it comes to parameterization coefficients, the irregular periodicity of the glottal excitation is a significant factor that leads to notable variations in the values of the feature vectors, resulting in disruptions in the amplitude spectrum with the appearance of ripples. In this study, a method is suggested to mitigate this phenomenon. To achieve this goal, inverse filtering was used to estimate the excitation and transfer functions of the vocal tract. Subsequently, using the derived parameterisation coefficients, statistical models for individual Polish phonemes were established as mixtures of Gaussian distributions. The impact of these corrections on the classification accuracy of Polish vowels was then investigated. The proposed modification of the parameterisation method fulfils the expectations, the scatter of feature vector values was reduced.