Speech Coding

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Speech Coding

• Submitted To Dr. Mohab Mangoud
• Submitted By Nidal Ismail

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Outline

• Introduction
• Overview of Speech Coding
• Properties of a Speech Coder
• Modeling the Speech Production System
• Linear Prediction
• Different Coding Techniques
• Waveform Coders
• Parametric Coders
• Hybrid Coders
• Coding Standards
• PCM DPCM
• Linear Predictive Coding
• Conclusion
• References

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1. Introduction
Overview of Speech Coding
Block Diagram of a speech coding system
Sampling Frequency 8kHz
Bit Rate 8 . 8kHz 64 kbps
Number of Bits per sample 8
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Properties of a Speech Coder
1. Introduction

• Low Bit-Rate
• High Speech Quality
• Robustness Across Different Speakers / Languages
• Robustness in the Presence of Channel Errors
• Good Performance on Non speech Signals
• Low Memory Size and Low Computational Complexity
• Low Coding Delay

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Modeling the Speech Production System
1. Introduction
Speech Voiced Unvoiced sounds
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Modeling the Speech Production System
1. Introduction
Autocorrelation values for the signal frames.
Left Unvoiced. Right Voiced.
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Modeling the Speech Production System
1. Introduction

• Signal from a source is filtered by a
  time-varying filter with resonant properties
  similar to that of the vocal tract.
• The gain controls Av and AN determine the
  intensity of voiced and unvoiced excitation.
• The frequency of higher formant are attenuated by
  -12 dB/octave (due to the nature of our speech
  organs).

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Linear Prediction
1. Introduction

• Linear prediction is a practical method of
  spectrum
• estimation, where the PSD can be captured using a
  few coefficients.
• These coefficients or linear prediction
  coefficients can be used to construct the
  synthesis filter.

Linear prediction as system identification.
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Linear Prediction
1. Introduction
Predicted Signal
Prediction error
Linear prediction as system identification.
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Outline

• Introduction
• Overview of Speech Coding
• Properties of a Speech Coder
• Modeling the Speech Production System
• Linear Prediction
• Different Coding Techniques
• Waveform Coders
• Parametric Coders
• Hybrid Coders
• Coding Standards
• PCM DPCM
• Linear Predictive Coding
• Conclusion
• References

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2. Different Coding Techniques

• Waveform Coders
• Original shape of the signal waveform is
  preserved
• Coders can be applied to any signal source
• Coders are better suited for high bit-rate
  coding, since performance drops sharply with
  decreasing bit-rate.
• In practice, these coders work best at a bit-rate
  of 32 kbps and higher.
• Some examples of this class include various kinds
  of pulse code modulation (PCM) and adaptive
  differential PCM (ADPCM)

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2. Different Coding Techniques

• Parametric Coders
• The speech signal is generated from a model,
  which is controlled by some parameters.
• Parameters are estimated from the input speech
  signal
• No attempt to preserve the original shape of the
  waveform
• Accuracy and sophistication of the mode account
  for the quality.
• The most successful model is based on linear
  prediction. In this approach, the human speech
  production mechanism is summarized using a
  time-varying filter ( with the coefficients of
  the filter found using the linear prediction
  analysis procedure.)
• This class of coders works well for low bit-rate.
  
• Bit-rate is in the range of 2 to 5 kbps.
• Example coders of this class include linear
  prediction coding (LPC) and mixed excitation
  linear prediction (MELP).

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2. Different Coding Techniques

• Hybrid Coders
• Combines the strength of a waveform coder with
  that of a parametric coder
• As in waveform coders, an attempt is made to
  match the original signal with the decoded signal
  in the time domain
• This class dominates the medium bit-rate coders,
  with the code-excited linear prediction (CELP)
  algorithm and its variants the most outstanding
  representatives
• A hybrid coder tends to behave like a waveform
  coder for high bit-rate, and like a parametric
  coder at low bit-rate, with fair to good quality
  for medium bit-rate.

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2. Different Coding Techniques
Coding Standards
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Outline

• Introduction
• Overview of Speech Coding
• Properties of a Speech Coder
• Modeling the Speech Production System
• Linear Prediction
• Different Coding Techniques
• Waveform Coders
• Parametric Coders
• Hybrid Coders
• Coding Standards
• PCM DPCM
• Linear Predictive Coding
• Conclusion
• References

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3. PCM DPCM

• Pulse Code Modulation

• Invented 1926, deployed 1962.
• Basic idea assign smaller quantization stepsize
  for small-amplitude regions and larger
  quantization stepsize for large-amplitude regions
  (Non-uniform Quantization)
• Two types of nonlinear compressing functions
• Mu-law adopted by North American
  telecommunications systems
• A-law adopted by European telecommunications
  systems
• Mu-law(A-law) compresses the signal to 8
  bits/sample or 64Kbits/second (without compandor,
  we would need 12bits/sample)

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3. PCM DPCM
Pulse Code Modulation

• ?-law

where A is the peak-input magnitude and ? is a
constant that controls the degree of compression.
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3. PCM DPCM
Pulse Code Modulation

• ?-law
• Examples

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3. PCM DPCM
Pulse Code Modulation

• A-law

with Ao a constant that controls the degree of
compression.
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3. PCM DPCM
Pulse Code Modulation

• A-law
• Examples

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3. PCM DPCM
Differential Pulse Code Modulation

• Since speech signals are slowly varying, it is
  possible to eliminate the temporal redundancy by
  prediction
• Quantizing the
• prediction-error Signal
• in are entered into the quantizers decoder to
  obtain the quantized prediction error, which is
  combined with the prediction xpn to form the
  quantized input.

DPCM encoder (top) and decoder (bottom)
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3. PCM DPCM
Differential Pulse Code Modulation

• Comparison between PCM and DPCM
• Half the bit rate was used in DPCM and a higher
  SNR was achieved

PCM quantized Signal (left) and Quantization
error (right)
DPCM quantized Signal (left) and Quantization
error (right)
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Outline

• Introduction
• Overview of Speech Coding
• Properties of a Speech Coder
• Modeling the Speech Production System
• Linear Prediction
• Different Coding Techniques
• Waveform Coders
• Parametric Coders
• Hybrid Coders
• Coding Standards
• PCM DPCM
• Linear Predictive Coding
• Conclusion
• References

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4. Linear Predictive Coding

• Linear prediction coding relies on a highly
  simplified
• model for speech production

• Parameters of the model are estimated from the
  speech samples

The LPC model of speech production
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4. Linear Predictive Coding

• Parameters of the model are estimated from the
  speech samples
• These include
• Voicing whether the frame is voiced or unvoiced.
• Gain mainly related to the energy level of the
  frame.
• Filter coefficients specify the response of the
  synthesis filter.
• Pitch period in the case of voiced frames, time
  length between consecutive excitation
  impulses.

The LPC model of speech production
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4. Linear Predictive Coding

• By carefully allocating bits for each parameter
  so as to minimize distortion, an impressive
  compression ratio can be achieved.
• For instance, the bit-rate of 2.4kbps for the
  FS1015 coder is 53.3 times lower than the
  corresponding bit-rate for 16-bit PCM
• Estimating the parameters is the responsibility
  of the encoder.
• The decoder takes the estimated parameters and
  uses the speech production model to synthesize
  speech

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4. Linear Predictive Coding
Block diagram of the LPC encoder.
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4. Linear Predictive Coding
Block diagram of the LPC decoder.
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4. Linear Predictive Coding

• The Voicing Detector is a key element to
  successful coding.
• The purpose of the voicing detector is to
  classify a given frame as voiced or unvoiced.
• Measurements that a voicing detector relies on to
• accomplish its task
• Energy

or

• Zero Crossing Rate

• Prediction Gain

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4. Linear Predictive Coding
Top left A speech waveform. Top right Magnitude
sum function. Bottom left Zero crossing rate.
Bottom right Prediction gain.
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4. Linear Predictive Coding
Samples/frame 180 samples
Bandwidth 2.4kbps
Frame Size 22.5ms 44.44 frames/sec
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4. Linear Predictive Coding
Speech Coder Standard Speech Coder Standard
FS1015-LPC10 Coefficient 10
FS1016-CELP Code Excitation
MELP Mixed Excitation
IS-54 VCELP Vector Sum Excited
IS-96 QCELP QualComm Code Excited
LD-CELP G.728 Low-Delay Code-Excited
G.729 CS-ACELP Conjugate-structure Algebraic-Code-Excited
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5. Conclusion

• An overview of speech coding was introduced with
  a brief explanation of the speech production
  model. Properties of different coding techniques
  were also co0mpared. For wire line transmission
  coding, PCM and DPCM were covered. Linear
  Prediction Coding which is a basic for modern
  wireless systems was also introduced.

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6. References

• Speech Coding Algorithms Wai C. Chu
• Digital Communications Bernard Skalr