Voice Coding Algorithms

There are several approaches to digitizing the voice samples. These approaches vary by the information that is transmitted, the complexity of the algorithm, and the assumptions of the sound being transmitted (e.g. voice, fax, music). Broadly classified, the various coding algorithms fall into two broad categories: coding of waveform and modeling of the vocal track. Pulse Code Modulation and Sub Band Coding are examples of waveform coding algorithms while Linear Predictive Coding is an example of an algorithm that models the vocal tract.

The Pulse Code Modulation (PCM) algorithm makes no assumptions about the sound that is being digitized and therefore does the best job on various types of sounds. It also produces the highest bit-rate for the data and has the shortest delay. The basics of the various PCM algorithms is that the voice is sampled at fixed time intervals (i.e. 8,000 times/second) and then a number is generated from the data based on each sample.

Figure 1. PCM algorithms sample the voice at fixed time intervals

ADPCM (Adaptive Differential Pulse Code Modulation), a variant of PCM, samples the voice at fixed time intervals and then calculates the change from the previous sample and sends that information. To save bandwidth, these step sizes are specially coded so that the step size at low volume is different than the step size at high volume. ADPCM provides about a 2:1 reduction in the data compared to PCM.

Figure 2. ADPCM codes the change in amplitude

A second approach to waveform coding is to digitally represent sounds using the frequency of the sounds. Instead of sampling the waveform in fixed units of time, the sound is represented in units of frequency. This works well for speech since vowels are low frequency and consonants are high frequencies. This type of algorithm is called a Sub Band Coder (SBC) and a spectrograph of speech using frequency is shown in Figure 3.

Figure 3. SBC uses frequencies of the voice sample

Another way to sample speech is to use a model of the way people generate speech. In the Linear Predictive Coding (LPC) algorithm, the human vocal tract is modeled. Humans have an excitation source at the source of the vocal tract and muscles along the tube is constricted which, in effect, shapes the waveform. People change the constriction points to make the various sounds (i.e. tongue and lip movement). LPC uses a series of filters that accomplish a similar function.

Sound reproduction can be very good and its performance is primarily limited by how well the excitation waveform can be reproduced. In the LPC algorithm, the filter coefficients and the excitation type are all that is needed to be transmitted which can be significantly less than the amount of information need to be transmitted for PCM methods.

The reduced bandwidth requirements of LPC come at the expense of the large amount of processing power necessary for the algorithm. LPC works well for sending human speech sounds, not very well for music and it does not work at all for transmitting fax (or computer modem) sounds.

There are also algorithms that use a mixture of these algorithms and produces adequate sound quality with medium bit rates. An example of such a hybrid coder is CELP.

Coding Standards

There are a number of voice coding standards and the ITU is the most active of the groups in this area. For information on the details of any of these standards, go to the ITU web site (www.itu.int/ITU-T ).

Table 1 provides a summary of several of the major voice coding algorithms. As can be seen, there is a range of data rates available. The column labeled MOS (Mean Opinion Score), is a subjective score that listeners give to each of these algorithms. For point of reference, G.711 is what is used in the US phone system.

Table 1. Voice Coding Standards

The complexity column of the above table is an indication of how complex the algorithm is to implement in a Digital Signal Processor (DSP). The exact value is not as important as is the relative numbers between the various algorithms.

G.711 (PCM)

G.711 (PCM: Pulse code modulation) is an international standard and widely used in the conversion of analog voice signals for use in digital transmission networks. The G.711 quality and characteristics are widely used as a reference point when new or improved algorithms are used in testing. Two sub-methods exist, mu-law (US) and A-law (non-US). G.711 has a 64 kbits/second data rate.

G.722 (SB-ADPCM)

The G.722 wideband speech coding algorithm uses SB-PCM (Sub-Band Adaptive Differential Pulse Code Modulation) and supports bit rates of 64, 56 and 48 kbps. The codec can be integrated on one chip and its overall delay is around 3 ms, small enough to cause no echo problems in telecommunication networks. In addition, this algorithm provides acceptable performance (maintains its intelligibility) for transmission bit error rates up to 10-3.

G.722 divides the 16 kHz sampled voice into two overlapping frequency bands. The coding of the sub-band signal is based on a modified version of ADPCM. Input samples in each band are adaptively predicted, quantized and transmitted.

High quality coding with the G.722 wideband speech coder is provided by a fixed bit allocation, where the low and high sub-bands ADPCM coders use a 6 bits/sample and 2 bits/sample quantizer, respectively. In the low sub-band the signal resembles the narrow-band speech signal in most of its properties and a high SNR in the lower band becomes perceptually more important than in the higher band.

G.723.1

ITU-T G.723.1 (G.721 + G.723 combined) produces digital voice compression levels of 20:1 and 24:1. It operates at 6.3 kbps and 5.3 kbps respectively. The only difference between these two transmission speeds is the amount of horsepower needed from the CPU.

The low bandwidth requirement is ideal for real time Internet telephony and usage over POTS-PSTN lines. G.723.1 has become one emerging standard for cross platform interoperability regarding the transmission of voice. Tests have shown acceptable quality with at 1/10 of the bandwidth compared to PCM.

The algorithm complexity is one that can be implemented in a PC. Combining this with the low bit rate, G.723.1 is the default low bit rate audio coder for the overall H.323 video conferencing standard.

G.726

ADPCM is able to provide good quality speech for bit rates of 32 Kbits/s. ADPCM has been standardized for bit rates of 16, 24, 32 and 40 Kbits/s. The ADPCM algorithm is different from PCM because of just sampling the voice data, the difference between the sampled voice data and the predicted speech signal is sent. With good prediction, the difference between the actual voice data and the predicted data will be small.

The adaptive quantizer does not have uniform step sizes. ADPCM can be changed to accommodate other sound characteristics besides voice.

G.728 (LD-CELP)

LD-CELP (Low Delay Code Excited Linear Prediction) is a European ITU-T variant of US federal standard 1016 for CELP. LD-CELP digitizes 4 KHz speech at 16 Kbps and low delay.

CELP divides the speech it is to code into 30ms frames, each of which is further divided into four 7.5 ms sub-frames. For each frame, the encoder calculates a set of 10 filter coefficients for the short-term synthesis filter that is used to model the vocal tract of the speaker. The excitation for this filter is determined for each sub-frame, and is given by the sum of scaled entries from two codebooks. An adaptive codebook is used to model the long-term periodicities present in voiced speech, and for each sub-frame, an index and a gain is determined for this codebook. There is a fixed codebook containing 512 pseudo-random codes that is also searched to find the codebook entry that minimizes the error between the reconstructed and the original speech samples.

At the decoder, the scaled entries from the two codebooks are passed through the synthesis filter to give the reconstructed speech. Finally, this speech is passed through a post filter to improve its perceptual quality.

G.729 (A) (CS-ACELP)

G.729 uses CS-ACELP coding (Conjugate Structure Algebraic Code Excited Linear Prediction) at 7 KHz at 8 Kbps with a frame size is 10 ms. CS-ACELP is just of a form of Linear Predictive Coding mentioned previously

I-30036 (GSM)

The GSM full rate speech codec operates at 13 kbits/s and uses a Regular Pulse Excited (RPE) codec. Basically the input speech is sampled at a 8 KHz sample-rate, split up into frames 20 ms long, and for each frame a set of 8 short term predictor coefficients are found. Each frame is then further split into four 5 ms sub-frames, and for each sub-frame the encoder finds a delay and a gain for the codec's long term predictor. Finally the residual signal after both short and long term filtering is quantized for each sub-frame as follows.

The 40 sample residual signal is decimated into three possible excitation sequences, each 13 samples long. The sequence with the highest energy is chosen as the best representation of the excitation sequence, and each pulse in the sequence has its amplitude quantized with three bits. At the decoder the reconstructed excitation signal is fed through the long term and then the short term synthesis filters to give the reconstructed speech. A post filter is used to improve the perceptual quality of this reconstructed speech.

The GSM codec generates good quality for speech, but the G.728 codec (CELP) still outperforms the GSM algorithm slightly with the higher rate. GSM codec is lighter, and can be run without DSP or special audio hardware in real-time.

More Information

Additional VoIP seminars:

An Introduction to VoIP - An overview of the VoIP technology, architecture, and the interconnection issues.

VoIP Applications - The VoIP technology only becomes useful when compelling applications meet the needs of customers. The corporate, cable telephony, and video conferencing applications are examined.

VoIP Problems - Deployment of VoIP has been slower than expected because of problems with underlying networks, standardization issues, and network control devices.

In Summary:

• There are different voice coding standards that provide tradeoffs in network bandwidth and computational complexity.

• Of the major voice coding algorithms, only G.711 can carry fax and modem signals.

• The voice coding algorithms with the lowest bandwidth work because of knowledge of the speech producing model and therefore do not work well on music..