Robust Formant tracking for 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. The algorithm proposed in this thesis achieves formant extraction from continuous speech by using a time-varying adaptive filterbank 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, for both male and female speakers in the presence of background noise. Thorough testing of the algorithm using various speech sentences has shown promising results over a wide range of SNRs for various types of background noises, such as AWGN, single and multiple competing background speakers and various other environmental sounds.