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Speech Parametrisation

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). Frames.

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Speech Parametrisation

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  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. 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 40ms 20ms

  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. 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!

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

  6. 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 2-dimensional Feature Vector ZCR STE

  7. 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

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

  9. 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

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

  11. DFT Spectrum /u/

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

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

  14. Preemphasis: using diff

  15. Preemphasis

  16. Windowing: using hamming

  17. Windowing: Spectral Effect

  18. LPC Spectrum: using lpc

  19. 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

  20. Cepstrally Smoothed Spectrum

  21. 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

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