Algorithms for Tracking Formant Frequencies of a Continuous Speech with Speaker Variability

Abstract

Exposure to loud sounds can cause damage to the inner ear, leading to degradation of the neural response to speech and to formant frequencies in particular. This may result in decreased intelligibility of speech. An amplification scheme for hearing aids, called Contrast Enhanced Frequency Shaping (CEFS), may improve speech perception for ears with sound-induced hearing damage. CEFS takes into account across-frequency distortions introduced by the impaired ear and requires accurate and robust formant frequency estimates to allow dynamic, speech-spectrum-dependent amplification of speech in hearing aids. Several algorithms have been developed for extracting the formant information from speech signals, however most of these algorithms are either not robust in real-life noise environments or are not suitable for real-time implementation. Two algorithms are discussed in the present work. One is Robust formant tracking algorithm and other is Recursive least square algorithm (RLS). The first algorithm achieves formant extraction from continuous speech by using a time-varying adaptive filter bank to track and estimate individual formant frequencies. The formant tracker incorporates an adaptive voicing detector and a gender detector for robust formant extraction from continuous speech. And the second algorithm is based on recursive least square values. Forgetting factor approach is used for estimating formant frequencies with RLS algorithm. Algorithms are tested for both male and female speakers in the presence of background noise. Thorough testing of the algorithms using various speech sentences has shown promising results over a wide range of signal to noise ratio's (SNR's) for various types of background noises, such as additive white gaussian noise, single and multiple competing background speakers and various other environmental sounds. Results from both algorithms showed that the robust formant tracking algorithm gives very good tracking performance in every environment and at every SNR. RLS algorithm also provide good estimate of formant frequencies in every environment but the estimate is not smooth and tracking of formant frequencies is very noisy. By observing the limitations of the traditional formant tracking algorithms and the present RLS algorithm it can be seen that the robust formant tracking algorithm is the most accurate algorithm and fulfill all the requirements for accurate formant tracking.