1 Speech Parametrisation Compact encoding of information in speech Accentuates important info –Attempts to eliminate irrelevant information Accentuates stable info –Attempts to eliminate factors which tend to vary most across utterances (and speakers)

2 40ms 20ms Frames Parameterise on a frame-by-frame basis Choose frame length, over which speech remains reasonably stationary Overlap frames e.g. 40ms frames, 10ms frame shift

3 Crude Parametrisation Time domain Use short-term energy (STE) Sequentially segment the speech signal into frames Calculate STE for each frame STE: n refers to the nth sample

4

5 Why not use waveform samples? How many samples in a frame? –The more numbers the more computation How can we measure similarity? Use what we know about speech… –F0? –Spectrum? –Formants?

6 Crude Parametrisation Frequency related Use zero-crossing rate (ZCR) Calculate ZCR for each frame: where:

7

8 Multidimensionality We can combine multiple features into a feature vector Let’s combine STE and ZCR and measure the magnitude of each feature vector More complex multidimensional feature vectors are generally used in ASR STE ZCR 2-dimensional Feature Vector

9

10 Sophistication We need something more representative of the information in the speech less prone to variation…e.g. spectra The spectral slices we have been viewing to date in Praat are actually LPC (Linear Predictive Coding) spectra LPC attempts to remove the effects of phonation –Leaves us with correlate of VT configuration More later…