This paper introduces a novel (HDAG - Harmonic Detection for Auditory Gain) method for speech intelligibility enhancement in noisy scenarios. In the proposed scheme, a series of selective Gammachirp filters are adopted to emphasize the harmonic components of speech reducing the masking effects of acoustic noises. The fundamental frequency values are estimated by the HHT-Amp (Amplitude-based Hilbert Huang Transform) technique. Harmonic components estimated with low accuracy are detected and adjusted according to the FSFFE (Frequency Separation for Fundamental Frequency Estimation) low/high pitch separation. The central frequencies of the filterbank are defined considering the third-octave subbands which are best suited to cover the regions most relevant to intelligibility. Before signal reconstruction, the gammachirp filtered components are amplified by gain factors regulated by FSFFE classification. The proposed HDAG solution and three baseline techniques are examined considering six background noises with four signal-to-noise ratios. Three objective measures are adopted for the evaluation of speech intelligibility and quality. Several experiments are conducted to demonstrate that the proposed scheme achieves better speech intelligibility improvement when compared to the competing approaches. A perceptual listening test is further considered and corroborates the objective results.

This paper introduces a novel method to separate voiced frames of noisy speech signals into low-frequency or high-frequency. This separation improves the accuracy of fundamental frequency (F0) estimators. In this proposal, the target signal is analyzed by means of the ensemble empirical mode decomposition. Next, the pitch information is extracted from the first decomposition modes. This feature indicates the frequency region where the speech F0 should be located, thus separating the frames into low-frequency or high-frequency. The frames separation is then applied to correct pitch candidates extracted from a F0 detection method, improving the estimation accuracy. The proposed method and a baseline separation approach are evaluated considering four different F0 estimation algorithms. Experiments are conducted with the CSTR and TIMIT databases, and six noises with various signal-to-noise ratios. The Gross Error (GE) and Mean Absolute Error (MAE) metrics are adopted to evaluate the solutions in terms of F0 estimation errors. Results show that the proposed method outperforms the baseline, in terms of low/high frequency separation accuracy. Moreover, the novel solution is able to better improve F0 detection accuracy under different noisy conditions.