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:

frame

nST sE 2

• n refers to the nth sample

4

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8-1

0

1

yes

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8-1

0

1

no

0 2 4 6 8 10 12 14 16 180

100

200

STE yesSTE no

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

Crude Parametrisation

• Frequency related

• Use zero-crossing rate (ZCR)

• Calculate ZCR for each frame:

frame

nnZCR

ssM

2

)(sign)(sign 1

• where:

01

01)(sign

x-

xx

7

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8-1

0

1

yes

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8-1

0

1

no

0 2 4 6 8 10 12 14 16 180

200

400

600

ZCR yesZCR no

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-dimensionalFeature Vector

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8-1

0

1

yes

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8-1

0

1

no

0 2 4 6 8 10 12 14 16 180

0.5

1

1.5

ZC_E yesZC_E no

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

Spectral Feature Extraction

• Extract compact set of spectral parameters (features) for each frame

• Frames usually overlapping

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

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

DFT Spectrum /u/

0 1000 2000 3000 4000 5000 6000-30

-20

-10

0

10

20

30

40

15

Frame Length:{5,40,200}ms

2 2.05 2.1 2.15 2.2 2.25

x 104

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

16

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

0 1000 2000 3000 4000 5000 6000-30

-20

-10

0

10

20

30

40

50

17

Preemphasis: using diff

2 2.01 2.02 2.03 2.04 2.05

x 104

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

18

Preemphasis

0 1000 2000 3000 4000 5000 6000-40

-30

-20

-10

0

10

20

30

40

19

Windowing: using hamming

2 2.01 2.02 2.03 2.04 2.05

x 104

-0.2

0

0.2

0.4

0.6

0.8

1

1.2Hamming Window

20

Windowing: Spectral Effect

0 1000 2000 3000 4000 5000 6000-60

-50

-40

-30

-20

-10

0

10

20

21

LPC Spectrum: using lpc

0 1000 2000 3000 4000 5000 6000-60

-50

-40

-30

-20

-10

0

10

20LP Order = 14

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

Cepstrally Smoothed Spectrum

0 1000 2000 3000 4000 5000 6000-60

-50

-40

-30

-20

-10

0

10

2025 Cepstral Coefficients

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