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Keep the words constant while changing the sound. Then ask questions whose answers depend on what changed: timing, speaker identity, emotion, background events, spatial position, music structure or relationships between clips. Run every item with the original audio, a silent version and transcript-only text. A model that succeeds only with the transcript, or scores the same when the audio is silent, is probably exploiting text rather than understanding the recording.
What the test must distinguish
Speech recognition answers what words were said. Audio understanding answers questions about the words and everything else carried by the waveform: who spoke, how they spoke, what happened between utterances, where a sound came from, which event occurred first, whether a background noise caused an outcome, and how two recordings differ. A useful evaluation therefore changes acoustic evidence without changing the transcript.
Audio understanding spans speech, non-speech sounds and music. It also includes temporal, spatial, prosodic, paralinguistic and cross-audio reasoning. MMSU (2026) illustrates the breadth with 5,000 audio-question-answer triplets across 47 tasks covering semantic, prosodic, phonological and paralinguistic phenomena. MMAU (ICLR 2025) contains 10,000 curated clips and 27 skills, while MMAU-Pro (AAAI 2026) expands evaluation to 5,305 instances and 49 skills, including long-form, spatial and multi-audio reasoning.
Why a transcript-only check is misleading
A transcript can make an audio question look solved even when the model never uses the recording. For example, the words may reveal that a meeting was cancelled, while only the audio reveals that the speaker was sarcastic, that a second person interrupted, or that an alarm sounded before the cancellation. A language model can answer the textual part from prior knowledge or from lexical cues.
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The strongest diagnostic is a control gap: compare performance on the same question with real audio, silence and transcript-only input. A genuine audio-dependent item should become harder when the waveform is removed. If transcript-only performance is nearly identical to real-audio performance, the item may not require hearing; if silent audio performs just as well, the audio channel is not contributing measurable evidence.
Define the capability axes before writing questions
Do not report one undifferentiated audio score. Tag every item by the capability it tests and analyze each tag separately.
| Capability | What changes in the audio | Example question | Textual shortcut to block |
|---|---|---|---|
| Temporal order and duration | Event order, pauses, overlap or elapsed time | Which sound happened first, and did the pause last longer than two seconds? | Keep the transcript identical while swapping event order or pause length. |
| Speaker attribution | Voice identity, turn-taking or overlapping speech | Which person said the final sentence? | Use the same words with speakers exchanged. |
| Emotion and prosody | Pitch, stress, speaking rate, hesitation and affect | Does the speaker sound relieved, angry or uncertain? | Record identical words in contrasting deliveries. |
| Non-speech events | Footsteps, alarms, impacts, engines, animals or room sounds | What happened immediately after the glass broke? | Use a transcript that contains no label for the event. |
| Causality | Event sequence and audible reaction | Did the warning tone occur before the machine stopped? | Keep spoken content fixed and reverse the sound sequence. |
| Spatial location | Left/right position, distance, movement or reverberation | Was the siren closer on the left channel or the right? | Render the same sound from different positions. |
| Multi-speaker relations | Interruption, agreement, call-and-response or simultaneous speech | Who interrupted whom? | Swap overlap timing while preserving every spoken word. |
| Music structure | Beat, instrumentation, melody, harmony or section changes | Does the chorus repeat, and which instrument enters first? | Provide lyrics that remain constant while arrangement changes. |
| Cross-clip comparison | Differences between two or more recordings | Which clip contains the longer pause? | Keep transcripts equal or deliberately uninformative. |
| Long-context reasoning | Events separated by minutes or many turns | What sound followed the announcement near the beginning? | Place distractors between the relevant events. |
Build transcript-controlled audio pairs
Transcript-controlled pairs are the core of the test. Each pair has the same words but a controlled acoustic difference. The question must be answerable from that difference, not from general knowledge.
1. Choose a minimal acoustic contrast
Start with one variable: calm versus irritated delivery, speaker A versus speaker B, a two-second versus six-second pause, a knock before versus after a sentence, or a sound rendered on the left versus right. Minimal contrasts make failures interpretable. Record or edit both clips so their lexical content, volume range and duration are otherwise comparable.
2. Write an audio-dependent question
Ask about the changed property directly. If the clips differ only in emotion, ask which speaker sounds anxious; do not ask what the speaker said. If they differ only in order, ask which event occurred first. Include answer options that are plausible from the transcript but wrong for the actual audio.
3. Verify the item with people
Have listeners who can access the audio answer the question and explain the evidence. Remove items that humans cannot resolve reliably, contain accidental textual clues, or have more than one defensible answer. Keep the transcript and audio files versioned so later edits cannot silently change the task.
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Run the three essential controls
- Real-audio condition: supply the recording and the question.
- Silent-audio condition: preserve the file format, duration and interface but replace the waveform with digital silence. This tests whether the model can answer from the prompt, metadata or expected answer pattern.
- Transcript-only condition: remove the audio and provide the verbatim transcript as text. This estimates the strength of a textual shortcut.
DCASE’s Audio-Dependency Filtering uses silent-audio probing, option-level checks, commonsense checks and human review to remove text-solvable items. Its resulting ADQA-Bench contains 3,000 items. Use the same control logic even when your own benchmark is much smaller.
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For multiple-choice questions, randomize option order independently in each condition. Otherwise a model can exploit a fixed answer position. For open answers, keep the prompt identical and ensure that the transcript shown in the control is exactly the one obtained from the audio, including hesitations when those are relevant.
Use adversarial distractors instead of easy labels
A distractor should be supported by the transcript but contradicted by the sound. Suppose the transcript says, “I am fine,” and the recording is clearly sobbing; include fine as an option only if the question asks about perceived emotion. For speaker attribution, use words that both speakers say elsewhere. For temporal questions, make every event appear in the transcript while changing only their order.
AUDITA follows this principle with human-authored real-world trivia, distractors and long-range temporal dependencies. It reported human accuracy of 32.13% versus below 8.86% for state-of-the-art audio-question-answering models, showing how much harder robust, audio-grounded questions can be than ordinary captioning.
Include entailment, not just fact questions
Audio Entailment frames evaluation as a three-way decision: the recording entails a hypothesis, contradicts it, or leaves it neutral. This format tests whether the model can reason from audible evidence without treating every plausible statement as true.
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Test retrieval-grounded audio reasoning separately
Some questions require both listening and outside information. AudioRAG evaluates this setting and reports that state-of-the-art models struggle in realistic web environments. Keep retrieval-dependent items in a separate category so a model is not penalized for lacking a browsing tool on a question intended to test perception.
For a retrieval item, first require an audio fact, then require a source-supported inference. For example, identify a song or spoken reference from the clip, retrieve a documented fact about it, and answer a question that combines the two. Score the audio identification and the evidence-grounded conclusion independently; a correct final guess should not hide a failed listening step.
Score real audio, silence and transcript conditions
For each capability, report accuracy in all three conditions and calculate two gaps:
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- Audio-to-transcript gap = real-audio accuracy minus transcript-only accuracy.
A large positive audio-to-silence gap indicates that the waveform matters. A small or negative gap is a warning that the item can be solved without hearing. A large transcript-only score with a small audio-to-transcript gap suggests the model is relying mainly on words. A model may legitimately score well on transcript-only semantic questions; that is why the benchmark must contain non-verbal and prosodic items.
Publish per-skill results, sample counts and uncertainty rather than a single headline average. For small sets, use bootstrap confidence intervals or another stated uncertainty method. Report the number of items in every slice; a 90% score on ten emotion questions is not comparable to a 70% score on 1,000 temporal questions.
Evaluate open-ended answers with human safeguards
Exact-match grading is unsuitable for many audio answers. “The speaker sounds worried” and “They seem anxious” may express the same judgment, while a fluent but unsupported explanation may be wrong. Use blinded human review or a calibrated judge that cannot see the model name or condition.
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- Define a short rubric for correctness, audio grounding and completeness.
- Have at least two raters score a sample and calculate inter-rater agreement.
- Adjudicate disagreements and preserve the original rationales.
- Inspect a random set of correct answers for evidence of guessing or transcript copying.
- Keep automated scores separate from human scores; AIR-Bench documents human-evaluation procedures and warns that automated evaluation can be biased.
Ask the model to cite the audible evidence in its explanation, but do not award points merely for mentioning a sound. A claimed footstep that is absent should count as a grounding error even when the final label happens to be correct.
Benchmark evidence: strong language ability is not enough
Published results illustrate why transcription quality cannot stand in for audio reasoning. MMAU reported Gemini 2.0 Flash at 59.93% and Qwen2-Audio at 52.50% on its demanding benchmark (ICLR 2025). MMAU-Pro reported Gemini 2.5 Flash at 57.33% and Audio Flamingo 3 at 45.9% (AAAI 2026). DCASE reported a top ADQA-Bench score of 58.33% and an average 11.93-percentage-point drop from development to hidden evaluation (2026).
| Benchmark or result | Published figure | What it demonstrates |
|---|---|---|
| MMAU | Gemini 2.0 Flash: 59.93%; Qwen2-Audio: 52.50% | Broad audio skills remain difficult even for capable multimodal models. |
| MMAU-Pro | Gemini 2.5 Flash: 57.33%; Audio Flamingo 3: 45.9% | Long-form, spatial and multi-audio reasoning expose additional weaknesses. |
| ADQA-Bench | Top score: 58.33%; development-to-hidden drop: 11.93 percentage points | Text shortcuts and benchmark familiarity can inflate development results. |
| AUDITA | Human accuracy: 32.13%; leading audio-QA models: below 8.86% | Human-authored distractors and long-range dependencies create a severe grounding challenge. |
These numbers are not directly comparable: the benchmarks use different clips, skills, prompts and scoring rules. Treat them as evidence that robust audio reasoning is unsettled, not as a universal ranking of intelligence.
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- Create 20 transcript-matched pairs: four each for timing, speaker identity, prosody, non-speech events and spatial or multi-clip comparison.
- Write one multiple-choice and one open-ended question for every pair, with at least one transcript-supported distractor.
- Generate real-audio, silent-audio and transcript-only versions, randomizing answer choices.
- Have independent listeners verify that each answer depends on the intended acoustic change.
- Run the model without revealing which condition it receives.
- Score exact choices automatically, review open answers blindly, and calculate the two control gaps for every skill.
- Publish the clips, transcripts, prompts, option order, model version, decoding settings, scoring rubric and uncertainty intervals so another evaluator can reproduce the result.
This pilot will not prove general understanding, but it quickly reveals whether a model can use the sound channel at all. Expand the set only after removing items that humans cannot solve or that the transcript alone answers.
Common failure patterns and what they mean
Real audio and silence scores are equal
The item is probably text-solvable, the silent file was not actually silent, or the model received metadata that exposed the answer. Inspect the waveform, remove filenames and duration cues that correlate with labels, and rewrite the question around an acoustic distinction.
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Transcript-only beats real audio
The transcript may be clearer than the recording, or the model may have a stronger text pathway. Check whether the audio is noisy, clipped or too long; then add a question whose answer is absent from the words. Do not interpret this result as proof that the model cannot hear in general.
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High multiple-choice accuracy but weak explanations
The model may be exploiting option wording or guessing. Randomize options, add adversarial distractors and require a concise evidence statement. Human reviewers should mark unsupported explanations as grounding failures.
Development results collapse on hidden clips
Look for memorization, prompt-specific strategies and leakage from public clips. Hold out speakers, environments, topics and recording sessions, and report the hidden-set result separately.
Raters disagree about emotion
Emotion is partly subjective. Use scenarios with a validated target when possible, permit an uncertain label, and report agreement rather than forcing a single ground truth. Separate recognition of vocal affect from inference about the speaker’s actual mental state.
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No benchmark proves general understanding. Results depend on item construction, answer format, evaluator bias, data leakage and model version. A model can perform well on a narrow set of synthetic contrasts yet fail on naturally occurring conversations, overlapping speakers or unfamiliar environments.
Use held-out audio, publish the exact clips and prompts, randomize choices, and provide confidence intervals or bootstrap uncertainty when sample sizes permit. Treat the result as a diagnostic profile: it tells you which acoustic evidence the tested model used under the tested conditions, not whether it possesses a universal human-like faculty.
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