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Articulation Index (AI)

The Articulation Index is a single-number metric that predicts the intelligibility of speech in the presence of background noise. Originally defined in ANSI S3.5, it quantifies the proportion of speech cues that are audible above the masking noise across critical frequency bands.

Theory

Speech intelligibility depends on the signal-to-noise ratio (SNR) across the frequency bands that carry speech information. The AI is computed as a weighted sum of the effective SNR contributions across these bands:

AI = ∑i=1N wi · Ai

where wi is the importance weighting for band i (reflecting how much that band contributes to speech understanding), and Ai is the effective audibility factor for that band.

Band Audibility Factor

The audibility factor for each band is derived from the SNR in that band, clipped to the range [0, 1]:

Ai = min(1, max(0, (SNRi + 12) / 42))

An SNR of −12 dB or below yields Ai = 0 (fully masked). An SNR of +30 dB or above yields Ai = 1 (fully audible). The 42 dB dynamic range corresponds to the useful range of speech-to-noise ratios for intelligibility.

Importance Weight (w) Frequency Band Center (Hz) AI Band Importance Weighting 0 .003 .006 .009 250 .0024 500 .0048 1k .0074 2k .0109 4k .0078 8k .0032 Peak: 2 kHz band

Frequency Band Weighting

The importance weights wi reflect the concentration of speech information across frequency. The bands centered around 1–3 kHz receive the highest weights because consonant discrimination depends primarily on this range.

Band Center (Hz)Weight (wi)Speech Content
2500.0024Vowel fundamentals, voicing cues
5000.0048First formant of most vowels
10000.0074Second formant, nasal consonants
20000.0109Fricatives, plosive bursts
40000.0078Sibilants (/s/, /z/), high-frequency consonants
80000.0032Upper harmonics, breathiness
The exact number of bands and their weights depend on the calculation method. The one-third octave band method uses 15 or more bands from 200 Hz to 5 kHz. The simplified table above illustrates the relative importance across the speech spectrum.

Interpretation

AI ValueIntelligibilityDescription
0.7 – 1.0ExcellentVirtually all speech cues are audible. Normal conversation is fully intelligible.
0.5 – 0.7GoodMost speech is understood. Some effort required in difficult listening conditions.
0.3 – 0.5FairSignificant portions of speech are masked. Listeners frequently ask for repetition.
0.1 – 0.3PoorOnly familiar phrases are recognized. Continuous communication is impractical.
0.0 – 0.1UnintelligibleSpeech is essentially inaudible in the noise.

Parameters

ParameterOptionsDescription
Input ChannelAny signal channelChannel carrying the combined speech-plus-noise or noise-only signal.
Noise SpectrumMeasured / ReferenceSource of the background noise spectrum for SNR computation.
Speech LevelNormal (60 dB), Raised (65 dB), Loud (75 dB)Assumed speech effort level at 1 m distance.
Calculation StandardANSI S3.5, ModifiedSelection of weighting and band definition.
Frequency Range200 Hz – 8 kHzAnalysis bandwidth for band decomposition.

AI vs RPM

In automotive NVH testing, the Articulation Index is tracked across the engine speed range to identify RPM conditions where cabin noise most severely impacts speech communication. The AI vs RPM curve reveals critical speeds where masking is worst, helping engineers target specific operating conditions for noise reduction.

Practical Tips

The Articulation Index is a predictive metric based on spectral SNR. It does not account for reverberation, temporal fluctuations, or informational masking. For environments with significant reverberation, the Speech Transmission Index (STI) may be a more appropriate metric.

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