Real-world speech is often corrupted by multiple degradations simultaneously, including additive noise, reverberation, and nonlinear distortion. Diffusion-based enhancement methods perform well on single degradations but struggle with compound corruptions. Prior noise-aware approaches inject conditioning at the input layer only, which can degrade performance below that of an unconditioned model. To address this, we propose injecting degradation conditioning, derived from a pretrained encoder with multi-task heads for noise type, reverberation, and distortion, into the timestep embedding so that it propagates through all residual blocks without architectural changes. In controlled experiments where only the injection method varies, input-level conditioning performs worse than no encoder at all on compound degradations, while layer-wise injection achieves the best results. The method also generalizes to diverse real-world recordings.
Figures & tables
Figure 1: Overview of SLICE. (a) The encoder produces h∈Rdh ; branch projections are concatenated and mapped to cextra∈Rd . (b) cextra is added to temb and propagated through every residual block. Dashed orange: auxiliary heads (train-only). dw=768 , dh=256 , db=128 , d=512 .
Noise-Only
Multi-Degradation
Method
PESQ
ESTOI
SDR
UTMOS
PESQ
ESTOI
SDR
UTMOS
MP-SENet [ 19 ]
3.16
0.88
19.6
3.90
2.39
0.62
− 0.3
3.00
MetricGAN+ [ 20 ]
3.13
0.83
0 8.6
3.63
2.53
0.62
− 5.5
2.82
SGMSE+ [ 1 ]
2.94
0.87
17.4
3.83
2.30
0.63
− 0.0
2.99
SGMSE+ (dereverb) [ 1 ]
1.98
0.78
10.9
3.16
1.99
0.66
− 3.3
2.96
NASE [ 12 ]
2.75
0.85
17.3
3.83
2.22
0.64
− 0.5
2.99
Table 1: Speech enhancement results on noise-only (824 files) and multi-degradation (2472 files) conditions. All baselines are trained on noise-only data. Controlled ablations are in Table 2 . Best per condition in bold . SDR denotes SI-SDR (dB).
Variant
PESQ
ESTOI
SDR
UTMOS
SLICE
2.60
0.80
3.7
3.71
w/o aux. loss ( λ=0 )
2.58
0.77
2.2
3.63
Input addition
2.49
0.73
1.4
3.62
No encoder
2.60
0.77
2.3
3.70
Zero conditioning
2.14
0.67
− 1.6
3.04
Uniform weights
2.60
0.80
3.7
3.71
Table 2: Results of the ablation study evaluated on the multi-degradation test set. The term “addition” refers to the input-level addition method introduced by [ 12 ] .
Type
N
PESQ
ESTOI
SDR
UTMOS
Noise only
824
2.82
0.86
17.5
3.93
Noise+Reverb
330
1.72
0.65
− 17.2
3.31
Noise+Distort
318
2.84
0.86
15.8
3.94
Noise+Reverb+Distort
338
1.74
0.65
− 18.4
3.34
Reverb only
341
2.01
0.74
− 17.1
3.42
Distort only
321
4.21
0.98
23.3
4.05
Table 3: Detailed performance breakdown across six distinct degradation categories within the multi-degradation test set.
Dataset
Model
PESQ
ESTOI
UTMOS
VOiCES
SGMSE+ (pretrained)
1.20
0.34
1.98
SGMSE+ (no enc.)
1.59
0.65
2.83
SLICE
1.56
0.67
2.77
DAPS
SGMSE+ (pretrained)
2.10
0.69
2.96
SGMSE+ (no enc.)
2.04
0.65
2.96
SLICE
2.20
0.60
3.32
Table 4: Evaluation on in-the-wild dataset. SGMSE+ (pretrained) is the publicly available denoise model [ 1 ] ; SGMSE+ (no enc.) is trained from scratch on the same multi-degradation data as SLICE but without an encoder. SI-SDR is omitted as it is unreliable for reverberant recordings.
Speech enhancement improves intelligibility and listening quality by suppressing noise, reverberation, and competing speakers in recorded or live audio.