1. 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. 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. 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. 4

5. 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… –Spectrum!

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

7. 7

8. 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. 9

10. 10 Parametrisation: Sophistication We need something more representative of the information in the speech less prone to variation 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

11. 11 Spectral Feature Extraction Extract compact set of spectral parameters (features) for each frame Frames usually overlapping

12. 12 DFT spectra vs LPC spectra DFT (Discrete Fourier Transform) –Technique ubiquitous in DSP for spectral analysis –fft function in MATLAB demo > Numerics> Fast Fourier Transform –Demo function dftdemo_sinusoid_sig LPC –Mathematical encoding of signals –Based on modelling speech as a series of sums of exponentially decaying sinusoids –Source-filter decomposition –Typical example of how spectral information can be compressed

13. 13 Preprocessing Speech for Spectral Estimation 1.Choose frequency resolution –Time/Frequency trade off –Parametrisation frame length 2.Pre-emphasise –Flattens spectrum which reduces spectral dynamic range which eases estimation 3.Apply window function in time domain –Tapers frame boundary values to zero –Gives better picture of spectrum

14. 14 DFT Spectrum /u/

15. 15 Frame Length:{5,40,200}ms

16. 16 Freq. Resolution for {5,40,200}ms

17. 17 Preemphasis: using diff

18. 18 Preemphasis

19. 19 Windowing: using hamming

20. 20 Windowing: Spectral Effect

21. 21 LPC Spectrum: using lpc

22. 22 LPC Linear Predictive Coding Rule of thumb for order –(kHz of Sampling Frequency) + (2 to 4) –In previous figure, order 14 was used LP Coefficients can be easily transformed to centre frequencies and bandwidths of peaks in spectrum MATLAB lpc –1st coefficient returned always 1, so omit

23. 23 Cepstrally Smoothed Spectrum

24. 24 MFCCs Mel Frequency Cepstral Coefficients –Encodes/compresses spectral info in approx. 12 coefficients –Weights areas of perceptual importance more heavily –Will use them in HTK –Other parameterisations possible