HAEPF: hybrid approach for estimating pitch frequency in the presence of reverberation.

In the realm of speaker identification, pitch frequency serves as a fundamental feature. However, this feature can be compromised when a speaker records his speech in a closed room, resulting in distorted signal features. This distortion not only reduces the effectiveness of speaker identification systems, but also opens the door for potential deception by hackers who exploit the reverberation effects in closed rooms. To address this concern, the correction of estimated pitch frequencies emerges as an essential step for the success of speaker identification systems. This paper presents a Hybrid Approach for Estimating Pitch Frequency (HAEPF) that integrates both the Zero Crossing Rate (ZCR) and Auto-Correlation Function (ACF) methods. Furthermore, the paper delves into the modeling of reverberant speech using comb filtering, shedding light on how multiple reflections impact the accuracy of pitch frequency estimation. Several simulation experiments were conducted to assess pitch frequency estimation for speech signals, both in the presence and absence of reverberation. The estimation errors were calculated for all three scenarios of reverberation (mild, moderate, and severe). The results clearly indicate that as the degree of reverberation, characterized by the comb filter order, increases, the pitch frequency estimation error also increases. The estimation accuracy of the proposed approach is calculated in terms of Pitch Frequency Estimation Error (PFEE), Gross Pitch Error (GPE) and Octave Error (OER) and is compared with those of several established pitch frequency estimation methods. The proposed approach exhibits a notable enhancement even in noisy environments, reducing PFEE by 43%, and achieving GPE and OER of less than 0.3 and 0.12, respectively, at a Signal-to-Noise Ratio (SNR) of 0 dB. [ABSTRACT FROM AUTHOR]