Recent Audio Multimodal Large Language Models (Audio MLLMs) demonstrate impressive performance on speech benchmarks, yet it remains unclear whether these models genuinely process acoustic signals or rely on text-based semantic inference. To systematically study this question, we introduce DEAF (Diagnostic Evaluation of Acoustic Faithfulness), a benchmark of over 2,700 conflict stimuli spanning three acoustic dimensions: emotional prosody, background sounds, and speaker identity. Then, we design a controlled multi-level evaluation framework that progressively increases textual influence, ranging from semantic conflicts in the content to misleading prompts and their combination, allowing us to disentangle content-driven bias from prompt-induced sycophancy. We further introduce diagnostic metrics to quantify model reliance on textual cues over acoustic signals. Our evaluation of seven Audio MLLMs reveals a consistent pattern of text dominance: models are sensitive to acoustic variations, yet predictions are predominantly driven by textual inputs, revealing a gap between high performance on standard speech benchmarks and genuine acoustic understanding.
Figures & tables
Figure 1: Illustration of the three acoustic–semantic conflict types in DEAF . ESC : happy text vs. depressed tone. BSC : quiet-setting text vs. noisy traffic audio. SIC : elderly-female text vs. young-boy voice. Correct prediction requires following the audio , not the text.
Figure 2: Word clouds of the happy class under Explicit and Implicit semantic conditions in ESC.
Figure 3: Overview of the DEAF dataset construction pipeline.
Type
Scripts
Match.
Mismatch.
Total
ESC
104
312
936
1,248
BSC
84
360
720
1,260 †
SIC
82
104
104
248 ‡
All
270
776
1,760
2,756
Table 1: Dataset statistics. † Across 5 SNR levels, including 180 neutral clips (36 per SNR level); single-SNR total is 252. ‡ Including 40 neutral clips shared across sub-dimensions.
Figure 4: Overview of the DEAF framework. Each conflict type is evaluated at three levels of increasing textual interference. Correct answers always require following acoustic evidence; trap answers follow textual cues.
Type
Question
ESC
What is the emotional tone of the speaker’s voice?
BSC
What environment do the background sounds suggest?
SIC G
What is the perceived gender of the speaker?
SIC A
How old does the speaker sound?
SIC C
What is the perceived gender and approximate age of the speaker?
Table 2: Question Templates.
ESC
BSC
SIC
Model
L1
L2
L3
Avg
L1
L2
L3
Avg
L1
L2
L3
Avg
Gemini 2.5 Flash
11.4
26.6
0.2
12.7
8.3
11.6
2.6
7.5
47.1
49.7
43.5
46.8
Gemini 3 Flash
5.2
2.5
0.0
2.6
7.8
16.2
2.0
8.7
43.1
42.2
34.0
39.8
GPT-4o-Audio
0.9
2.5
0.0
1.1
1.3
4.8
0.0
2.0
1.7
0.0
6.5
2.7
Audio Flamingo 3
24.8
30.8
6.6
20.7
14.5
16.5
4.6
11.9
44.6
15.0
43.4
34.3
Qwen2-Audio
14.7
34.0
5.8
18.2
5.5
20.5
3.5
9.8
40.0
30.2
32.6
34.3
Table 3: Acoustic Robustness Score (ARS, %) across conflict types and levels. Higher values indicate stronger acoustic grounding under textual interference.
Figure 5: Radar comparison of acoustic perception performance across conflict types.
ESC
BSC
SIC
Model
L1
L2
L3
Avg
L1
L2
L3
Avg
L1
L2
L3
Avg
Gemini 2.5 Flash
6.1
15.9
0.1
7.4
4.3
6.2
1.3
3.9
54.7
52.2
34.1
47.0
Gemini 3 Flash
2.7
1.3
0.0
1.3
4.3
10.1
1.0
5.1
37.9
30.6
23.7
30.7
GPT-4o-Audio
0.4
1.3
0.0
0.6
0.7
2.4
0.0
1.0
0.9
0.0
3.4
1.4
Audio Flamingo 3
14.6
19.2
3.4
12.4
7.9
9.1
2.3
6.4
66.4
8.2
31.9
35.5
Qwen2-Audio
8.1
22.2
3.0
11.1
2.9
12.9
1.8
5.9
32.8
19.4
22.0
24.7
Table 4: Accuracy (Acc, %) on mismatched samples across conflict types and levels.
Model
L1
L2
L3
Gemini 2.5 Flash
6.7
0.7
2.2
Gemini 3 Flash
3.5
-6.3
1.7
GPT-4o-Audio
1.1
-2.4
0.0
Audio Flamingo 3
2.5
1.3
3.9
Qwen2-Audio
0.2
3.0
-1.2
Qwen3-Omni
10.0
-0.9
12.2
Table 5: The Environment Discrimination Index (EDI) for BSC.
Model
ESC
BSC
SIC
Closed-source
Gemini 2.5 Flash
−1.8
+0.7
−3.5 †
Gemini 3 Flash
+2.6 †
+0.4
−1.5
GPT-4o-Audio
+0.8
+0.0
+1.1
Open-source
Audio Flamingo 3
−8.9 ‡
−5.0 †
−2.5
Table 6: Δ ARS (%, Explicit − Implicit) across conflict types. Significance is assessed with bootstrap tests (10,000 resamples); † denotes p<0.01 and ‡ denotes p<0.001 .
Condition
Acc
TDS
OR
Correct prompt
93.6
–
6.4
L1
1.0
64.7
34.3
L2
2.9
94.8
2.4
L3
0.1
99.7
0.2
Table 7: GPT-4o-Audio response breakdown on ESC (%), as assigned by the LLM judge. Correct prompt: the L1 clips with a prompt stating the correct emotion (e.g., “The speaker sounds happy.”). Since no textual cue points to a wrong answer, all incorrect responses are counted as OR. All rows come from a single evaluation run, so Acc may differ slightly from Table 4 .
Appendix figures & tables6 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 6: Word clouds of the three datasets: (a) ESC, (b) BSC, and (c) SIC.
Category
Code
Description
Domestic
DWASHING
Laundry room
DKITCHEN
Kitchen, food prep
DLIVING
Living room
Nature
NFIELD
Sports field
NRIVER
River, flowing water
NPARK
City park
Appendix
Table 8: BSC environment categories from DEMAND.
ESC
BSC
SIC
Model
L1
L2
L3
Avg
L1
L2
L3
Avg
L1
L2
L3
Avg
Gemini 2.5 Flash
89.0
81.6
96.2
88.9
91.1
89.7
94.0
91.6
41.4
47.4
60.3
49.7
Gemini 3 Flash
95.4
96.9
98.3
96.9
39.6
41.0
41.4
40.7
50.0
68.1
59.9
59.3
GPT-4o-Audio
94.0
96.4
98.8
96.4
90.2
89.4
90.8
90.1
35.8
43.1
64.7
47.9
Audio Flamingo 3
81.1
77.9
92.7
83.9
91.4
87.3
97.2
92.0
33.6
91.8
68.1
64.5
Qwen2-Audio
79.3
72.1
87.5
79.6
62.6
50.0
77.8
63.5
51.3
68.5
62.9
60.9
Appendix
Table 9: Acoustic Sensitivity Score (ASS, %) across conflict types and levels. Higher values indicate greater sensitivity to acoustic changes. ASS is computed from changes in judge labels (C/T/O) and is an upper bound on class-level response change (Section 2.5 ).
Voice Profile
Voice ID
Young Male
1wzJ0Fr9SDexsF2IsKU4
Young Female
aFueGIISJUmscc05ZNfD
Elderly Male
Av4Fi2idMFuA8kTbVZgv
Elderly Female
0rEo3eAjssGDUCXHYENf
Appendix
Table 10: ElevenLabs voice profiles used for speech synthesis.
Subtask
n
Human ACC
Human ACC
Krippendorff
items
(full vote)
(high-conf, ≥ 3-agree)
α
ESC
100
51.7%
69.4%
0.35
BSC
100
75.0%
88.4%
0.68
SIC
100
100.0%
100.0%
1.000
Overall
300
75.6%
86.0%
0.72
Appendix
Table 11: Human evaluation results across different subtasks.