Wavelet Parameterization for Speech Recogniti0n:Variations in Translation and Scale Parameters
Richard E Favero · 1994
This paper describes two expenments to isolate the processes of using the wavelet parameterisation with a Hidden Markov Model (HMM) for speech recognition. Previous work with the wavelet transform and the HMh4 highlighted a local maximum in recognition performance for varying frame advance and initial wavelet filter length. The first experiment varies the initial wavelet filter length (frequency resolution) with a fixed frame advance (fixed time sampling).This shows a local maximum with increasing initial wavelet filter length (increasing frequency resolution).The second experiment varies the frame advance (time sampling) with a fixed initial wavelet filter length (fixed frequency resolution). This shows recognition performance decreasing monotonically with increasing the frame advance.