Word recognition device
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Word Recognition Device. C.K. Liang & Oliver Tsai. Why is speech recognition important?. Several real world applications. Dictation devices/software i.e. Dragon Naturally Speaking.

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Word Recognition Device

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Word recognition device

Word Recognition Device

C.K. Liang & Oliver Tsai


Why is speech recognition important

Why is speech recognition important?

  • Several real world applications.

  • Dictation devices/software i.e. Dragon Naturally Speaking.

  • Voice activated devices may be used to dial telephone numbers, change preset buttons in car audio, change t.v. stations, and several other possibilities.


How is this possible

How is this possible?

  • Linear Predictive Coding (LPC)

  • LPC models waveform like Infinite Impulse (IIR) Filter.

  • Uses the feedback from past inputs and past outputs to predict future outputs


Iir filter

IIR Filter

a(1)*y(n) = b(1)*x(n) + b(2)*x(n-1) + ...+b(nb+1)*x(n-nb)

- a(2)*y(n-1)-…-a(na+1)*y(n-na)


How do we use lpc for speech recognition

How do we use LPC for speech recognition?

  • Record human speech

  • Pre-emphasis

  • Convolution pre-emphasis filter with waveform


Pre emphasis filter

Pre-emphasis Filter


Why are vowel sound used

Why are vowel sound used ?


Hamming window

Hamming Window

  • Multiply the 240 samples point by point with hamming window

  • Reduce the amplitude on both ends of the window frame


Waveform of a consonant sound

Waveform of a consonant sound


Variance

Variance

Sound analysis summary

LPC Coefficients


General block diagram

General Block Diagram

A/D converter

8000 samples/sec

Pre-emphasis

filter

Frame Blocking

30ms window framing

Hamming

Window

Levinson-Durbin

Algorithm

Auto-Correlation

SSD Comparison

Output

4 digital bits


Implementation on motorola dsp56303

Implementation on Motorola DSP56303

  • Train Device for vowel sound template

  • Recognition Device for vowels


Training for sound template

Training for sound template

  • Detect beginning of speech

  • Pre-emphasize 2000 input samples

  • Hamming window 240-sample frame

  • Calculate 10 LPC coefficients

  • Repeat 10 times and store 10 sets of LPC coefficients


Recognition device

Recognition Device

  • Detect beginning of speech

  • Pre-emphasize 2000 input samples

  • Create window frame by shifting 80 samples

  • Hamming window each frame

  • Find 10 LPC coefficients for each frame

  • Compute SSD between the coefficients and those in template


Output hardware

Output Hardware

Map 4 output bits from DSP board to 10 corresponding vowel LEDs plus 1 volume indicator LED with NAND chips


Difficulties encountered

Difficulties encountered

  • Insufficient data memory

  • Indirect connection between microphone and the DSP board

  • Incompatible I/O core302 assembly file

  • Low volume for the sound input


Further expansion

Further Expansion

  • Speech compression

  • Large vocabulary continuous speech recognition with Hidden Markov Model


Autocorrelation

H(Z) = G/(1+A1 Z-1+A2 Z-2 + …. + A10 Z-10)

239

Ri =  x(n) x(n-i)

n=i

for i = 1 to 10

Autocorrelation


Levinson durbin algorithm

Levinson-Durbin Algorithm

R0R1R2 …. R9 A1R1

R1 R0R1 ….R8A2R2

R1 R0R1 ….R8A3= -R3

……………………….….

R9 R8R7 ….R0A10R10

An(i) = An-1(i) + Kn An-1(n-i)

Kn = (-1/En-1)  An-1(I) Rn-i (i = 0 to n-1)

En = En-1 (1-Kn2 )


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