Harmonics-to-noise ratio estimation with deterministically time-varying harmonic model for pathological voice signals

The harmonics-to-noise ratio (HNR) and other spectral noise parameters are important in clinical objective voice assessment as they could indicate the presence of nonharmonic phenomena, which are tied to the perception of hoarseness or breathiness. Existing HNR estimators are built on the voice signals to be nearly periodic (fixed over a short period), although voice pathology could induce involuntary slow modulation to void this assumption. This paper proposes the use of a deterministically time-varying harmonic model to improve the HNR measurements. To estimate the time-varying model, a two-stage iterative least squares algorithm is proposed to reduce model overfitting. The efficacy of the proposed HNR estimator is demonstrated with synthetic signals, simulated tremor signals, and recorded acoustic signals. Results indicate that the proposed algorithm can produce consistent HNR measures as the extent and rate of tremor are varied. (C) 2022 Acoustical Society of America.

Automated summary

This paper proposes the use of a deterministically time-varying harmonic model to improve the HNR measurements, demonstrating its efficacy with synthetic signals, simulated tremor signals, and recorded acoustic signals, resulting in consistent HNR measures as the extent and rate of tremor are varied.