<![CDATA[The pitch period is a key component of many speech analysis research projects. In real-world applications, voice data is frequently gathered in noisy surround- ings, therefore algorithms must be able to manage background noise well in order to estimate pitch accurately. Despite advancements, many state-of–the-art algorithms struggle to deliver adequate results when faced with low signal-to- noise ratios (SNRs) in processing noisy speech signals. This research proposes an effective concept specifically designed for speech processing applications, particularly in noisy conditions. To achieve this goal, we introduce a fundamen- tal frequency extraction algorithm designed to tolerate non-stationary changes in the amplitude and frequency of the input signal. In order to improve the extrac- tion accuracy, we also use a cumulative power spectrum (CPS) based on discrete cosine transform (DCT) rather than conventional power spectrum. We enhance extraction accuracy of our method by utilizing shorter sub-frames of the input signal to mitigate the noise characteristics present in speech signals. According to the experimental results, our proposed technique demonstrates superior per- formance in noisy conditions compared to other existing state-of-the-art meth- ods without utilizing any kind of post-processing techniques.]]>
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