Speech Coding Submitted To: Dr. Mohab Mangoud Submitted By: Nidal Ismail

Outline 1.Introduction  Overview of Speech Coding  Properties of a Speech Coder  Modeling the Speech Production System  Linear Prediction 2.Different Coding Techniques  Waveform Coders  Parametric Coders  Hybrid Coders  Coding Standards 3.PCM & DPCM 4.Linear Predictive Coding 5.Conclusion 6.References

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

Properties of a Speech Coder  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 1. Introduction

Modeling the Speech Production System 1. Introduction Speech = Voiced + Unvoiced sounds

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

1. Introduction Modeling the Speech Production System Signal from a source is filtered by a time-varying filter with resonant properties similar to that of the vocal tract. The gain controls A v and A N 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).

1. Introduction Linear Prediction Linear prediction as system identification.  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.

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

Outline 1.Introduction  Overview of Speech Coding  Properties of a Speech Coder  Modeling the Speech Production System  Linear Prediction 2.Different Coding Techniques  Waveform Coders  Parametric Coders  Hybrid Coders  Coding Standards 3.PCM & DPCM 4.Linear Predictive Coding 5.Conclusion 6.References

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)

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). 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. 2. Different Coding Techniques

Coding Standards

Outline 1.Introduction  Overview of Speech Coding  Properties of a Speech Coder  Modeling the Speech Production System  Linear Prediction 2.Different Coding Techniques  Waveform Coders  Parametric Coders  Hybrid Coders  Coding Standards 3.PCM & DPCM 4.Linear Predictive Coding 5.Conclusion 6.References

3. PCM & DPCM Pulse Code Modulation  Invented 1926, deployed  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)

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

 -law Examples 3. PCM & DPCM Pulse Code Modulation

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

A -law Examples 3. PCM & DPCM Pulse Code Modulation

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  i[n] are entered into the quantizer’s decoder to obtain the quantized prediction error, which is combined with the prediction x p [n] to form the quantized input. DPCM encoder (top) and decoder (bottom)

3. PCM & DPCM Differential Pulse Code Modulation PCM quantized Signal (left) and Quantization error (right) DPCM quantized Signal (left) and Quantization error (right)  Comparison between PCM and DPCM  Half the bit rate was used in DPCM and a higher SNR was achieved

Outline 1.Introduction  Overview of Speech Coding  Properties of a Speech Coder  Modeling the Speech Production System  Linear Prediction 2.Different Coding Techniques  Waveform Coders  Parametric Coders  Hybrid Coders  Coding Standards 3.PCM & DPCM 4.Linear Predictive Coding 5.Conclusion 6.References

4. Linear Predictive Coding  Linear prediction coding relies on a highly simplified model for speech production The LPC model of speech production  Parameters of the model are estimated from the speech samples

4. Linear Predictive Coding The LPC model of speech production  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.

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

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

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

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

4. Linear Predictive Coding Top left: A speech waveform. Top right: Magnitude sum function. Bottom left: Zero crossing rate. Bottom right: Prediction gain.

4. Linear Predictive Coding Bandwidth: 2.4kbps Samples/frame : 180 samples Frame Size: 22.5ms = frames/sec

4. Linear Predictive Coding Speech Coder Standard FS1015-LPC10Coefficient 10 FS1016-CELPCode Excitation MELPMixed Excitation IS-54 VCELPVector Sum Excited IS-96 QCELPQualComm Code Excited LD-CELP G.728Low-Delay Code-Excited G.729 CS-ACELPConjugate-structure Algebraic-Code-Excited

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.

6. References  Speech Coding Algorithms “Wai C. Chu”  Digital Communications “Bernard Skalr”