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A measure of differences in speech signals by the voice timbre

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Measurement Techniques Aims and scope

Abstract

This research relates to the field of speech technologies, where the key issue is the optimization of speech signal processing under conditions of a prior uncertainty of its fine structure. The problem of automatic (objective) analysis of the speaker’s voice timbre using a speech signal of finite duration is considered. It is proposed to use a universal information-theoretic approach to solve it. Based on the Kullback-Leibler divergence, an expression was obtained to describe the asymptotically optimal decision statistic for differentiating speech signals by the voice timbre. The author highlights a serious obstacle during practical implementation of such statistics, namely: synchronization of the sequence of observations with the pitch of speech signals. To overcome the described obstacle, an objective measure of timbre-based differences in speech signals is proposed in terms of the acoustic theory of speech production and its “acoustic tube” type model of the speaker’s vocal tract. The possibilities of practical implementation of a new measure based on an adaptive recursive filter are considered. A full-scale experiment was set up and carried out. The experimental results confirmed two main properties of the proposed measure: high sensitivity to differences in speech signals in terms of voice timbre and invariance with respect to the fundamental pitch frequency. The obtained results can be used when designing and studying digital speech processing systems tuned to the speaker’s voice, for example, digital voice communication systems, biometric and biomedical systems, etc.

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Notes

  1. The assumption of a Gaussian probability distribution does not limit the generality of the conclusions of this study, as this law is characterized by the maximum entropy for a given average power of the speech signal.

  2. COSH—cosine hyperbolic function.

  3. Researchers often prefer Berg’s method over other parametric spectral analysis methods due to its well-known advantages in terms of computational speed and, most importantly, stability of the spectral estimates of the autoregressive type that are formed on its basis.

  4. The Phoneme Training phonetic analysis and speech training information system: [website]. URL: https://sites.google.com/site/frompldcreators/produkty-1/phonemetraining (access date: May 18, 2023).

  5. This order is intended for autoregressive simulation of 4–5 AFC resonances of a typical vocal tract when pronouncing vowels in the frequency bandwidth of 0 to 4 kHz.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Nizhny Novgorod, Russian Federation

    V. V. Savchenko

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Translated from Izmeritel’naya Tekhnika, No. 10, pp. 63–69, October, 2023. Russian DOI: https://doi.org/10.32446/0368-1025it.2023-10-63-69.

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Original article submitted September 18, 2023; approved after reviewing October 18, 2023; accepted for publication October 18, 2023.

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Savchenko, V.V. A measure of differences in speech signals by the voice timbre. Meas Tech 66, 803–812 (2024). https://doi.org/10.1007/s11018-024-02294-1

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