SNOAR: Self-training with Noise filtering, Observation adding, and Remixing for Speech Denoising Without Clean References

Samuele Cornell; Matthew Maciejewski

eess.ASOct 14, 2026

SNOAR: Self-training with Noise filtering, Observation adding, and Remixing for Speech Denoising Without Clean References

Authors: Samuele Cornell, Matthew Maciejewski

Organizations: Carnegie Mellon University, USA · Johns Hopkins University, USA

Abstract

Unsupervised speech denoising methods such as MixIT and noisy-target training can be trained directly on noisy recordings, avoiding the sim-to-real gap of synthetic training data, but remain well below supervised performance. Teacher--student self-training, as in RemixIT, narrows this gap by remixing a teacher's estimates into new mixtures that supervise a student; however, since the student is trained only toward the teacher's estimates, teacher errors, in particular over-suppressed speech, are never recovered and propagate as the teacher is updated from the student. We propose SNOAR, a self-training framework that corrects the teacher's estimates before use: the noise leaked into its speech estimate is removed by differential noise filtering (DNF), and part of the noisy observation is added back in to restore suppressed speech and attenuate artifacts. We show that the two corrections depend on each other, as without DNF the loop collapses to the identity mapping. Compared to RemixIT, SNOAR is more stable along training and outperforms it in signal-based, perceptual, and word error rate metrics, reaching 97% of the SI-SDR improvement of a fully-supervised topline on WHAM!; on real CHiME-3 recordings, it improves every metric of three different teachers, while RemixIT degrades their perceptual quality.

Figures & tables

Panel of Figure 1 — Figure 1: The SNOAR self-training framework. From a real recording ${\mathbf{x}}_{i}$ the EMA teacher produces a speech and a noise estimate; the speech estimate is filtered against the noise estimate by DNF (Eq. 2 ) and combined with the observation by OA (Eq. 3 ), giving the corrected self-supervision $\hat{\mathbf{s}}^{\,\mathrm{oa}}_{i}$ . This is remixed with the noise estimate of another recording ${\mathbf{x}}_{j}$ to form the student input. The student is trained to reconstruct $\hat{\mathbf{s}}^{\,\mathrm{oa}}_{i}$ and $\hat{\mathbf{n}}_{j}$ , and the teacher is updated from the student weights by EMA.

System	SI-SDR	STOI	PESQ	D-OVRL	UTMOS	WER
Unprocessed	$-1.48$	$0.751$	$1.10$	$1.46$	$1.38$	$19.8$
Clean	$\infty$	$1.000$	$4.64$	$3.33$	$4.22$	4.7
Supervised topline	$13.68$	$0.952$	$2.26$	$3.21$	$3.60$	9.5
NyTT [ 6 ]	0.20	$0.776$	$1.13$	$1.60$	$1.36$	$18.1$
NyTT+DNF [ 14 ] , teacher	4.87	$0.847$	$1.32$	$2.43$	$2.05$	$17.7$
RemixIT(NyTT+DNF)	9.32	$0.913$	$1.56$	$2.61$	$2.67$	$13.1$

Table 1: Results on the WHAM! test set. SNOAR and RemixIT are listed under the teacher they were initialised from (MixIT or DNF). For SI-SDR, the 95% confidence interval obtained via paired bootstrap over the test utterances has a half-width of at most

0.08

dB for every system.

Figure 2: SNOAR(MixIT) and RemixIT(MixIT) at every training checkpoint, scored on a randomly selected subset of 500 WHAM! cv utterances.

Arm	SI-SDR	PESQ	UTMOS	WER	$\alpha$	$\lambda$	$r$
MixIT teacher	8.32	$1.54$	$2.26$	$15.3$	$0.912$	$0.218$	$0.085$
SNOAR(MixIT), $\gamma{=}0.2$	13.25	2.02	$3.45$	10.9	$0.949$	$0.031$	$0.048$
RemixIT(MixIT)	$12.10$	$1.61$	$3.15$	$13.3$	$0.939$	$0.026$	$0.062$
$\gamma{=}0.5$	$-0.82$	$1.11$	$1.38$	$19.9$	$0.998$	$0.899$	$0.033$
$\gamma{=}0.1$	$13.14$	$1.87$	3.47	$11.1$	$0.946$	$0.020$	$0.050$
$\gamma{=}0$ (no OA)	$12.33$	$1.84$	$3.27$	$11.2$	$0.944$	$0.018$	$0.059$

Table 2: Ablation of SNOAR(MixIT) on the WHAM! tt set. The first row is the method as described (

\gamma=0.2

\alpha

\lambda

and

r

are the speech, noise and artifact terms of the decomposition of Sec. 2 , with

r

the artifact energy as a fraction of the estimate’s.

Teacher	System	D-SIG	D-BAK	D-OVRL	UTMOS	WER
	Unprocessed	$2.05$	$1.50$	$1.48$	$1.38$	$22.0$
MixIT (real)	Teacher	$2.77$	$2.01$	$1.94$	$1.62$	$22.1$
	RemixIT	$2.67$	$1.95$	$1.87$	$1.57$	$20.3$
	SNOAR	3.09	2.37	2.19	2.10	18.4
Mix+sup (WSJ)	Teacher	$3.13$	$2.46$	$2.28$	$2.03$	$17.1$
	RemixIT	$2.70$	$2.01$	$1.91$	$1.69$	$18.4$

Table 3: Results on CHiME-3 et05_real , channel 5. No clean reference exists for these recordings, so only reference-free scores are reported. Each block is one in-domain teacher, trained without clean in-domain speech but allowed out-of-domain clean speech from WHAM! or LibriSpeech, followed by SNOAR and RemixIT adapted from it with the same budget of 7.5k steps.

Speech Enhancement

Speech enhancement improves intelligibility and listening quality by suppressing noise, reverberation, and competing speakers in recorded or live audio.

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Speech Enhancement

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In speech enhancement, standard supervised training requires pairs of noisy and clean speech. Such pairs are typically created by artificially degrading clean speech, leading to a domain mismatch between synthetic training data and real recordings. Noisy target training (NyTT) mitigates this limitation by training an enhancement model to predict a noisy recording from a noisier version generated by adding extra noise. However, since the training target is noisy, NyTT might learn to leave residual noise in the enhanced output. In this paper, we address this issue in NyTT by incorporating a Sinkhorn drifting loss that matches the distribution of the enhanced speech to that of unpaired clean speech. The Sinkhorn drifting loss attracts enhanced speech toward high-density regions of the clean-speech distribution while repelling it from the current enhanced-speech distribution in latent space. This loss uses only unpaired clean speech rather than the clean counterpart of each degraded recording, preserving the weakly-supervised nature of NyTT. Experimental results on VoiceBank-DEMAND and CHiME-3 real recordings demonstrate the efficacy of the proposed combination.

Abstract

Speech Enhancement

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