A new algorithm is presented for real-time fundamental frequency estimation of speech signals. This method extends and alters the YIN algorithm, which uses the autocorrelation-based difference function, by adding features to reduce latency, correct predictable errors, and make it structurally appropriate for real-time processing scenarios. The algorithm is shown to reduce the error rate of its predecessor while demonstrating latencies sufficient for real-time processing. The results indicate that the algorithm can be realized as a real-time estimator of spoken pitch and pitch variation, which has applications including diagnosis and biofeedback-based therapy of many speech disorders.
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