science

Perceptual Speech Quality Measure

Perceptual Speech Quality Measure is a science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Perceptual Speech Quality Measure rather than just read about it. In short: Perceptual Speech Quality Measure (PSQM) is a computational and modeling algorithm defined in Recommendation ITU-T P.861 that objectively evaluates and quantifies voice quality of voice-band (300 – 3400 Hz) speech codecs. It may be used to rank the performance of these speech codecs with differing speech input levels, talkers, bit rates and transcodings.

Key takeaways

Perceptual Speech Quality Measure belongs to science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Perceptual Speech Quality Measure to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Perceptual Speech Quality Measure from memory before moving on to harder problems.

Reference excerpt

Perceptual Speech Quality Measure (PSQM) is a computational and modeling algorithm defined in Recommendation ITU-T P.861 that objectively evaluates and quantifies voice quality of voice-band (300 – 3400 Hz) speech codecs. It may be used to rank the performance of these speech codecs with differing speech input levels, talkers, bit rates and transcodings. P.861 was withdrawn and replaced by Recommendation ITU-T P.862 (PESQ), which contains an improved speech assessment algorithm.

Why it is used Using the PSQM standard allows automated, simulation-based test methodologies to objectively rate both speech clarity and transmitted voice quality. Various software and/or hardware products have been developed to facilitate this testing. This results in considerable savings in cost and time over the traditional practice of using large groups of people to subjectively evaluate voice signals and assess voice quality. Moreover, it yields objective results that are reliable and reproducible. This is very important to telephony providers who are mandated to maintain high quality-of-service standards.

Algorithm PSQM uses a psychoacoustical mathematical modeling (both perceptual and cognitive) algorithm to analyze the pre and post transmitted voice signals, yielding a PSQM value which is a measure of signal quality degradation and ranges from 0 (no degradation) to 6.5 (highest degradation). In turn, this result may be translated into a mean opinion score (MOS), which is an accepted measure of the perceived quality of received media on a numeric scale ranging from 1 to 5. A value of 1 indicates unacceptable, poor quality voice while a value of 5 indicates high voice quality with no perceptible issues. The PSQM algorithm converts the physical-domain signal(s) into the perceptually meaningful psychoacoustic domain through a series of nonlinear processes such as time-frequency mapping, frequency warping and intensity warping. The quality of the coded speech is judged on the differences in the internal representation. The difference is used for the calculation of the noise disturbance as a function of time and frequency. Besides perceptual modeling, the PSQM algorithm uses cognitive modeling such as loudness scaling and asymmetric masking in order to get high correlations between subjective and objective measurements.

Limitations PSQM, as originally conceived, was not developed to account for network quality of service perturbations common in Voice over IP applications, effects such as packet loss, delay variation and out-of-order delivery. These conditions usually give inappropriate results under heavy network load simulations, failing to account for a very real perceived loss of voice quality. Attempts to duplicate network fault conditions by introducing significant packet loss result in PSQM values that correspond to falsely inflated MOS values. In order to overcome this limitation, PSQM+ was developed by modifying the original algorithm. PSQM+ generates results that seem to more accurately reflect the adverse performance of speech codecs under realistic network load conditions.

Other considerations Other issues involve the lack of standardization in test signals used to evaluate various speech codecs. PSQM provides more reliable and consistent MOS values if used in accordance with ITU recommended methods for objective and subjective assessment of quality (ITU-T P.800/P.830/P.861). These ITU-T Recommendations include using both male and female gender voice reference signals at an average level of −20 dB. The type, gender, duration, gain of the voice or signal can all have a minor impact on the PSQM value or MOS score as does the threshold levels, number of calls made and other configuration settings of the environment. When comparing voice quality measurements the signal, environment and configurations should all be taken into account. Many speech codecs exist and are used in a wide variety of applications. Careful selection of appropriate speech codec(s) is necessary to match system requirements. A list of common speech codecs and their associated PSQM/PSQM+ derived MOS values obtained under various network load conditions is available.

References ITU-T Recommendation P.861 (withdrawn): Objective quality measurement of telephone-band (300–3400 Hz) speech codecs. P.861 was recognized as having certain limitations in specific areas of application. It was replaced by P.862, which contains an improved objective speech quality assessment algorithm. ITU-T Recommendation P.862 (2001-02): Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs "AES Journal Forum » A Perceptual Speech-Quality Measure Based on a Psychoacoustic Sound Representation". secure.aes.org. Retrieved 2024-04-18.

See also Mean opinion score (MOS) Perceptual Evaluation of Speech Quality (PESQ), the successor technology for PSQM Voice over IP

Worked examples

Example 1 — a first encounter with Perceptual Speech Quality Measure

Start with the simplest possible case. Write down what Perceptual Speech Quality Measure claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Perceptual Speech Quality Measure before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Perceptual Speech Quality Measure ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Perceptual Speech Quality Measure

In research: Perceptual Speech Quality Measure appears in science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Perceptual Speech Quality Measure in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Perceptual Speech Quality Measure is common in secondary-school and first-year university syllabi. It links to neighbouring topics ITU-T P Series Recommendations, ITU-T recommendations, International standards, so understanding it makes those chapters shorter.
In everyday life: Look for Perceptual Speech Quality Measure outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Perceptual Speech Quality Measure in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Perceptual Speech Quality Measure means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Perceptual Speech Quality Measure out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Perceptual Speech Quality Measure in simple terms?

Why does Perceptual Speech Quality Measure matter?

Because it connects several science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Perceptual Speech Quality Measure?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Perceptual Speech Quality Measure.