29 Voice Compression: Samples, Companding, and Speech Models

29.1 Start With the Decision

A raw voice stream is large. A speech model keeps the sound useful while it cuts the bit rate.

29.2 Route Overview

This is part 2 of 2. Review Voice Compression: Codec and Link Constraints for the preceding evidence.

29.3 Learning Objectives

• Calculate raw PCM bit rate from sample rate, bit depth, and channel count.
• Compare companding and speech-model codecs against link delay and loss.

29.4 Chapter Roadmap

• Under the Hood: From Samples to a Speech Model
• Summary
• Key Takeaway
• See Also

29.5 Under the Hood: From Samples to a Speech Model

The deeper layer explains the bit-rate baseline, how companding squeezes more from each bit, and how coders reach low rates.

Why Compression Works

Compression starts from redundancy. English text is not random: spaces, vowels, common letters such as E, T, A, O, I, and N, and repeated word patterns appear far more often than rare letters or unusual sequences. A compressor can spend short codes on common events and longer codes on rare events. Speech signals have similar structure across amplitude, time, and frequency. Adjacent samples are often related, voiced speech has periodic energy, and different frequency bands carry different amounts of intelligibility.

Voice codecs also exploit source knowledge. A speech source is shaped by the lungs, vocal folds, vocal tract, tongue, lips, and nasal passages, and the listener's ear and auditory system do not treat every sample error equally. That does not mean a codec can throw away anything it likes; it means compression should be judged against the human task. The useful review dimensions are compression efficiency, reconstructed speech quality, algorithmic latency, error resilience on a lossy link, and computational complexity on the target device.

The PCM Baseline

Uncompressed linear PCM has a simple bit rate:

The classic narrowband telephony baseline samples speech at 8 kHz with 8-bit samples on one channel:

By the sampling rule, an 8 kHz rate represents content up to about 4 kHz, which is enough for intelligible speech though narrower than the full range of hearing. This baseline is the reference that compression improves upon.

Companding: More From the Same Bits

Speech has a wide dynamic range, and quiet passages still carry meaning. Linear quantization spreads its code steps evenly, wasting resolution on loud parts. Companding applies a logarithmic curve before quantizing and the inverse curve after, giving quiet sounds finer steps. The two classic companding laws let 8-bit companded speech sound like more bits of linear PCM at the same bit rate.

In G.711 terms, mu-law and A-law are the two logarithmic companding rules used for narrowband PCM telephony. Both map a larger internal sample range into an 8-bit transmitted code word, then expand it at playback. Mu-law is commonly associated with a 14-bit two's-complement input range before companding, while A-law is commonly described from a 13-bit signed input range; the operational point is the same for a connected product review: the wire still carries a 64 kbps channel, but the quantization steps are spent where speech perception needs them most.

Pause at the figure Figure 29.1 before applying Companding: More From the Same Bits. Its Companding shapes quality, not payload size and Linear PCM labels show why Companding changes how code steps are spent across speech levels; it improves perceived quality without changing the payload rate needs an evidence check for Companding: More From the Same Bits here.

Inspect Figure 29.1 at Companding shapes quality, not payload size and note that it uses Companding shapes quality, not payload size to show the next hand-off. Compare Linear PCM, which highlights Linear PCM, before reading Log encode as the place that uses Log encode to show the next hand-off. The comparison turns Companding changes how code steps are spent across speech levels; it improves perceived quality without changing the payload rate into a bounded Companding: More From the Same Bits choice.

Digital Telephony Baseline

Classic digital telephony is a useful baseline because it separates the data plane from the control plane. The data plane is the sampled speech stream: one narrowband channel at 8 kHz and 8 bits/sample is a 64 kbps voice channel before packet headers. Mu-law and A-law are the classic companding laws that make those 8-bit samples more useful for speech than uniform linear steps. T1 and E1 trunks then schedule many of those voice channels into fixed time slots with time-division multiplexing, rather than dedicating one analog path per conversation.

The control plane carries the evidence that sets up or steers the conversation. DTMF dialing is the compact example: each keypad symbol is encoded as one low-group tone and one high-group tone, so switching equipment or an IVR can recognize digits without interpreting speech. For IoT voice and audio systems, this distinction is still practical: keep control events, call state, media packets, timestamps, and authentication evidence separate enough that audio loss does not silently become a command or routing failure.

Carrier telephony made that split explicit in SS7, the signaling system used around the public switched telephone network. A service switching point attaches phones and call paths, a signal transfer point routes signaling messages through the control network, and a service control point hosts service logic or database lookups. The useful IoT transfer is not the acronym set; it is the habit of asking which component carries media, which component routes control evidence, and which database or policy service is allowed to change the session.

Toll-free and emergency services show why that control plane is more than call setup. A dialed 1-800 number can be translated through a service database before the voice path is connected, so the caller's digits, the lookup result, and the media circuit are separate evidence streams. Vertical service codes such as *69, *66, and *60 follow the same pattern: a small user command changes switch behavior only if the service record and policy allow it. Enhanced 911 adds the public-safety version of the lesson by routing a call toward the right public safety answering point and address record, often through Master Street Address Guide style data. A 108-style emergency service such as GVK EMRI makes the same architecture visible outside the US: the short code, call-center screen, ambulance dispatch, medical triage, police or fire handoff, and callback record are one safety workflow, but each field has a different owner and failure mode. For IoT designs, this is the warning against "solutions looking for problems": powerful service logic must expose the problem it solves, the database it trusts, and the failure mode when the lookup is stale, unavailable, or routed to the wrong authority.

The later computer-telephony shift moved some service logic away from large carrier switching platforms from vendors such as Nortel, Siemens, Ericsson, Lucent/Bell Labs, and NTT and toward PCs, servers, and application APIs. Computer telephony integration, including TAPI-style interfaces, let software coordinate call state with message machines, interactive voice response menus, and call-center workflows. For connected products, treat a voice prompt, keypad digit, support script, or dashboard button as a control action with an owner, log, timeout, and fallback path, not merely as audio.

That shift also explains why simple answering machines matter in an IoT course. A local recorder with remote playback became a network service that can store a message, notify another endpoint, expose a directory, route a caller through an IVR, and attach the result to a customer record. Modern unified-communications systems bundle voice, email, video, presence, directory services, data, and mobility around the same session evidence. Call centers and CRM systems then add queues, agent screens, support scripts, outsourced handoff, and 411-style directory assistance. The design lesson is to keep the voice path, the control event, the identity record, and the business workflow explicit, because a support operator or automation service should see enough evidence to help without silently gaining access to unrelated device telemetry.

A caution follows from the same CTI path: once software owns the voice interface, release evidence must include more than audio quality. A speech interface often chains voice activation, speech recognition and transcription, intent interpretation, data lookup, and spoken response. Each stage needs a failure budget: noise immunity and low power at activation, recognition accuracy for real callers, intent confidence and escalation rules, query authorization, and response confirmation. A 99.999% availability target still allows about five minutes of annual downtime, so emergency and public-safety voice paths cannot treat a cloud IVR, carrier route, or call-center integration as always available. 911 outages and phone-compromise scenarios belong in the review as explicit drills: verify fallback dialing, local instructions, callback identity, audit logs, least-privilege support screens, and a security path for suspected PBX, handset, or account takeover.

Lossless Versus Lossy

Lossless coding reconstructs the exact samples but achieves only modest, data-dependent compression, so it rarely fits tight voice links. Lossy coding discards perceptually minor information for much larger savings and is the norm for voice.

Two Ways to Be Lossy

A parametric coder reaches a low bit rate because it transmits a compact model and lets the receiver re-synthesize speech, rather than sending the signal itself. That is also why it distorts music or tones, which the vocal-tract model was never meant to represent.

Waveform coders improve on plain PCM without fully switching to a speech model. Adaptive differential PCM, or ADPCM, predicts the next sample, sends a quantized prediction error, and adapts the predictor and quantizer as the signal changes. If the prediction is good, the error needs fewer bits than the original sample. Subband ADPCM first splits the speech bandwidth into bands, often with quadrature mirror filters, then codes the bands separately so each band can use a rate and predictor that match its energy and perceptual value. These designs sit between simple waveform preservation and full parametric speech synthesis.

A parametric speech-production model goes further. It treats speech as excitation from a voiced pulse train or unvoiced noise source passing through a vocal-tract filter and a lip-radiation/output model. Linear predictive coding estimates that filter from recent speech samples, then sends parameters such as prediction coefficients, pitch, gain, and voiced/unvoiced state. The decoder reconstructs intelligible speech from the model parameters, which is why this family can reach very low rates but must be validated with real voices, accents, noise, packet loss, and device CPU limits.

The LPC review record should name what is being estimated, not only quote a low bit rate. A short speech segment is approximated as a weighted sum of previous samples plus an excitation term. The encoder estimates the prediction coefficients for an all-pole vocal-tract filter, the residual or source signal that drives it, and the gain of that excitation. A practical implementation often estimates pitch from the autocorrelation of low-pass filtered speech, marks voiced segments as periodic and unvoiced segments as noise-like, and solves the coefficient equations with efficient Toeplitz-matrix methods such as Levinson-Durbin recursion. This is the source-filter reason an LPC-style coder can spend roughly 0.5 to 1.5 bits per sample rather than sending every PCM sample.

Code-excited linear prediction, or CELP, keeps the LPC source-filter model but searches a codebook for an excitation vector that minimizes a perceptually weighted error after the pitch predictor and LPC synthesis filter. That idea sits behind many mobile speech codecs: the channel sends a compact description of model state and excitation instead of a full waveform. In an IoT review, names such as GSM full-rate, AMR, AMR-WB, EVRC, or EVRC-B should trigger the same questions: supported modes, selected bit rate, voice activity behavior, packet-loss concealment, CPU load, and whether the codec is being used only for speech-like audio.

Latency, Overhead, and Loss

• Frame latency. Block-based codecs process audio in frames, so the frame length, any look-ahead, and the processing time all add delay before a packet can be sent. This algorithmic delay is separate from network delay.
• Packet overhead. Each audio frame is small, so fixed per-packet headers can be a large fraction of the bytes on the wire; batching frames amortizes the header but raises latency and per-loss audio.
• Loss propagation. Coders that predict across frames can let one lost packet affect following frames until the decoder re-synchronizes; packet-loss concealment hides gaps, and codec and packet-size choices set how gracefully the link degrades.

Voice Quality Evidence

Voice quality is not proven by bit rate alone. Objective measures such as signal-to-noise ratio, segmental SNR, PESQ, and POLQA can compare a reference speech signal with a degraded output, but they still need listening context. The human-facing scale is often a mean opinion score, where 5 means excellent, 4 good, 3 fair, 2 poor, and 1 bad. A release record should say which language, talkers, background noise, packet loss, and device audio path were tested, because a codec that scores well in clean English lab speech may fail under accents, low-cost microphones, or wireless loss.

Variable-rate mobile codecs make that evidence especially important. EVRC-style operation can pair a high rate with voiced speech, lower rates with unvoiced or silence periods, and transitional rates with onsets, saving radio capacity while preserving intelligibility. AMR and AMR-WB modes make similar trade-offs across rates and bandwidths. The useful claim is therefore not "we use AMR" or "we use EVRC"; it is the selected mode set, the switching rule, the average data rate under realistic conversations, the MOS or objective-quality evidence, and the failure behavior when frame erasures rise.

VoIP Over Wireless

Packet voice adds protocol overhead around a small coded payload. A circuit-switched model dedicates capacity to a call, while a packet-switched model usually stacks codec output, presentation or payload framing, RTP, UDP, IP, MAC, and PHY behavior. That stack adds headers, algorithmic delay, packetization delay, arrival jitter, and fragmentation or goodput loss risk. Robust Header Compression, or ROHC, can make repeated IP/UDP/RTP headers much smaller on constrained wireless links, but the review should record the before-and-after header budget, compressor context, packet-loss tolerance, and recovery after long gaps or reordered packets. For wireless IoT products that carry voice, the packet budget should include the codec payload, RTP timestamp and sequence evidence, UDP/IP headers, link-layer retries, security overhead, jitter-buffer delay, and loss-concealment behavior.

The standards named in a voice design tell reviewers where to look. RTP itself is specified by RFC 3550, the basic audio/video profile by RFC 3551, AMR and AMR-WB RTP payload formats by RFC 4867, and EVRC-family RTP payload formats by RFC 5188. These references do not make a deployment correct, but they keep the record precise: the endpoint should identify the payload format it actually negotiates, the packet time it uses, the timestamp clock, and how receiver reports or application telemetry prove delay, jitter, and loss.

Common Pitfalls

1. Validating on a tone or signal-to-noise figure instead of real speech. Objective tone metrics miss intelligibility and conversational delay.
2. Ignoring algorithmic latency. A low-bit-rate codec can still feel laggy if its frames and look-ahead add delay.
3. Forgetting per-packet overhead. For tiny audio frames, headers can dominate the link.
4. Over-batching audio per packet. It cuts overhead but worsens latency and the impact of each loss.
5. Using a speech-model coder for non-speech audio. Vocal-tract models assume speech and distort music or tones.

PCM Bit-Rate Knowledge Check

At this depth, voice coding is a budget problem: a PCM baseline set by sampling and bit depth, companding and lossy coding to shrink it, and a balance of bit rate, latency, overhead, and loss that only a real-link listening test can confirm.

29.6 Summary

• Voice compression shrinks a bandwidth-hungry audio stream so speech fits a narrow, often lossy, IoT link.
• The path is sample, quantize, then compress, and lossy coding is normal because speech only needs to stay intelligible.
• The classic narrowband PCM baseline is 8 kHz, 8-bit, single channel, which is 64 kbps before compression.
• Companding improves perceived quality at the same bit rate by giving quiet sounds finer quantization steps.
• Digital telephony keeps voice payloads, multiplexing, and control signals such as DTMF conceptually separate; SS7, toll-free lookup, vertical service codes, CTI/TAPI, IVR, enhanced 911 routing, unified communications, directory assistance, CRM-linked call centers, 911 fallback, and phone-compromise drills show why IoT audio designs should preserve that boundary across gateways, applications, and support workflows.
• Waveform coders reproduce the signal shape at moderate rates, while speech-model coders send vocal-tract parameters to reach much lower rates for speech only.
• Codec choice must balance bit rate, quality, algorithmic latency, packet overhead, and loss behavior, and must be validated with real speech over the real link.

29.7 See Also

29.8 Continue Your Route

This final part closes the route from Under the Hood: From Samples to a Speech Model through See Also. Return to Voice Compression: Codec and Link Constraints or continue from the fundamentals module index.