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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

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
hamming window
Hamming Window
  • Multiply the 240 samples point by point with hamming window
  • Reduce the amplitude on both ends of the window frame
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

R0 R1 R2 …. R9 A1 R1

R1 R0 R1 …. R8 A2 R2

R1 R0 R1 …. R8 A3 = - R3

…………………… …. ….

R9 R8 R7 …. R0 A10 R10

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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