arXiv · 2025 · Preprint
Yurii Halychanskyi et al. (University of Illinois Urbana-Champaign) · → Paper · Demo: ? · Code: ?
Introduces a diffusion-based foreign accent conversion framework where the initial noise level acts as a user-controllable dial between preserving the original non-native pronunciation and converting toward a native-sounding target accent.
Problem
Prior accent conversion (AC) and foreign accent conversion (FAC) systems modify pronunciation, and sometimes prosody, toward a fixed target accent but offer no explicit control over how strongly the conversion is applied. Because accent is entangled with perceived speaker identity, a system that always pushes fully toward the target accent forces a fixed, non-adjustable trade-off between nativeness and identity preservation. This is limiting because different applications want different points on that trade-off: language learning may want maximal native-like pronunciation, dubbing needs duration and rhythm preserved for synchronization, and personal communication may want only a light touch. Existing AC baselines also require parallel accented training data, which is scarce and accent-specific.
Method
The system operates on the content sub-latent of FACodec (introduced by NaturalSpeech 3), a neural speech codec that factorizes an intermediate encoder representation into content, prosody, and acoustic-detail latents plus a global timbre embedding. The content latent is itself split into two residuals; the framework denoises only the first, 8-dimensional quantized content residual, leaving prosody, acoustic detail, and timbre latents fixed so that only pronunciation is affected.
Training uses only native speech with transcripts (LJSpeech, a single Standard American English speaker): a 6-layer, 8-head Transformer denoiser (model dimension 1024, feed-forward dimension 2048) is trained with a standard diffusion noise-prediction objective under a linear noise schedule (T=100 steps), conditioned on phoneme-aligned transcripts via FiLM and additive embeddings (alignment obtained with Wav2Vec2 XLSR). No parallel accented data is required at training time. A second small network predicts the codec’s second content residual from the denoised first residual and shared encoder features, trained jointly with weight 0.5 to preserve content fidelity after the codec’s residual split.
At inference, adapting the SDEdit noise-controlled editing paradigm to speech, a non-native utterance’s content latent is corrupted with noise up to a chosen start timestep t_start, then denoised back toward the learned native-pronunciation prior using deterministic DDIM (ODE) sampling for K=100 steps. Because each 20ms latent frame is denoised independently with its own posterior, the amount of noise (t_start) directly controls how far the posterior mean is pulled from the non-native input toward the native prior: small t_start keeps output close to the source, large t_start biases strongly toward native-like pronunciation, and intermediate values produce a smooth, frame-level mixture of native and non-native segments that reads as a gradual perceptual shift. After denoising, the codec’s second content residual is predicted, the first residual is snapped to its nearest codebook entry, prosody/acoustic-detail/timbre latents are kept from the source, and FACodec decodes the combined latents to a waveform.
Key Results
On L2-Arctic (24 non-native speakers across 6 accents), increasing t_start from reconstruction (no conversion) to 100 (strongest conversion) raises the average American-accent classifier score from 70.51 to 89.86 (+19.35), while average speaker similarity (WavLM x-vector cosine similarity) falls from 0.98 to 0.88 (-0.10) and average WER rises from 0.05 to 0.15 (+0.10), confirming a smooth, controllable trade-off between accent conversion strength, identity preservation, and intelligibility (Table 1). The effect size depends on the source accent: speakers already closer to American English (Spanish, Korean) show small or negative accent-score shifts, while speakers further away (Hindi, Vietnamese) shift the most.
Compared against three prior AC systems on subsets derived from their public demo pages (baselines’ source code was unavailable), the proposed method matches or exceeds a pronunciation-only baseline on speaker similarity (0.88 vs. 0.80 average) and WER (0.07 vs. 0.10 average) with comparable or higher accent-classifier scores (Table 2). Against two baselines that additionally regenerate prosody and duration, the proposed method matches or exceeds speaker similarity and WER but reaches substantially lower accent-classifier scores (e.g., 46.51 vs. 79.83 average against one baseline), which the authors attribute to prosody regeneration contributing more to perceived nativeness than pronunciation change alone. A MUSHRA-like listening test with 13 participants corroborates the objective trend: perceived similarity to Standard American English increases progressively with t_start, though the authors note the small participant pool means the subjective results should be read as indicative rather than conclusive.
Novelty Assessment
The individual components are borrowed: SDEdit-style noise-controlled diffusion editing, FACodec’s factorized codec, and DDIM sampling are all established techniques from prior work in image editing, TTS editing, and codec-based speech synthesis. The contribution is the specific adaptation of noise-controlled editing to a single, disentangled codec sub-latent for accent conversion, plus the training/inference recipe that requires no parallel accented data. The paper’s own framing (any-to-one AC that needs only native speech for training and provides a tunable strength parameter) is a genuine, if narrow, capability that prior AC systems did not offer. The evaluation is modest in scale: baseline comparisons rely on small, non-standardized subsets scraped from competitors’ demo pages (as few as 4 samples per accent) rather than a shared benchmark, and the subjective test has only 13 participants, both of which limit how strong a claim can be drawn about relative system quality.
Field Significance
Moderate — the paper demonstrates a genuinely new controllable mechanism for foreign accent conversion strength, built by adapting a diffusion noise-editing paradigm to a factorized codec’s content latent. Its contribution is architectural but narrow: it targets a specific sub-problem (pronunciation-only accent modification with a strength dial) rather than a broader synthesis capability, and the evaluation scale (small baseline subsets, 13-participant listening test) constrains how far the results generalize.
Claims
- supports: Operating a diffusion noise-editing procedure on a single, disentangled codec sub-latent (holding other codec latents fixed) allows fine-grained control over one speech attribute without needing an explicit disentanglement loss beyond what the pretrained codec already provides.
Evidence: Only the FACodec content residual z_c1 is noised and denoised, while prosody, acoustic-detail, and timbre latents remain fixed at their source values; this keeps accent modification confined to pronunciation while leaving prosody and speaker timbre unaffected by design. (§2.2, §3.5)
- supports: The initial noise level in a diffusion posterior can serve as a continuous, user-controllable interpolation parameter between an input-faithful and a prior-faithful output.
Evidence: Sweeping t_start from 25 to 100 produces a monotonic, smooth increase in accent-classifier score (70.51 to 89.86 average) with a corresponding smooth decrease in speaker similarity (0.98 to 0.88), verified both objectively and in a MUSHRA-like listening test. (§5.1, §5.3, Table 1, Figure 2)
- complicates: Accent classifiers used as automatic evaluation proxies can misjudge nativeness on individual samples, undermining the reliability of small-sample objective comparisons.
Evidence: For a 4-sample Korean baseline subset, the accent classifier labeled two outputs as Canadian rather than American, producing a large negative accent-score delta despite the speech perceptually sounding close to North American English. (§5.2)
- complicates: Preserving pronunciation while leaving prosody unchanged trades away some of the perceived nativeness gains that prosody regeneration provides, even when objective speaker-similarity and intelligibility metrics favor the pronunciation-only approach.
Evidence: Against baselines that regenerate prosody and duration, the proposed method achieves equal or better WER and speaker similarity but substantially lower accent-classifier scores (e.g., 46.51 vs. 79.83 average against one baseline), and the paper notes its timbre-based speaker-similarity metric does not capture the loss of speaker-specific prosodic traits that regeneration baselines incur. (§5.2)
Limitations and Open Questions
Warning
Baseline comparisons use small, non-standardized evaluation subsets (as few as 4 samples per accent) reconstructed from competitors’ public demo pages because baseline source code was unavailable, and results across different baselines are explicitly stated by the authors as not directly comparable to each other or to the full L2-Arctic evaluation.
The subjective evaluation draws on only 13 participants with limited per-listener overlap (10 of 340 trials each), which the authors themselves describe as indicative rather than conclusive. The system is trained on a single native speaker (LJSpeech) reading prepared text, so the learned “native prior” reflects one speaker’s reading style rather than a broad distribution of native pronunciation. The accent classifier used for automatic evaluation is a black-box model with potential dataset or accent biases, and the paper explicitly restricts its use to relative trend analysis rather than absolute quality claims. The authors identify perceptually guided noise (concentrating corruption on strongly accented regions) and coordinated suprasegmental control (jointly regenerating prosody and acoustic-detail latents) as future directions not addressed in this work.
Wiki Connections
- Voice Conversion — applies a voice-conversion-style disentangled content/speaker/prosody pipeline to the specific sub-task of foreign accent conversion, adding an explicit strength control absent from prior AC systems.
- Diffusion TTS — adapts SDEdit-style noise-controlled diffusion editing (originally used for image synthesis and speech editing) to iteratively denoise codec content latents toward a native-pronunciation prior.
- Neural Audio Codec — operates directly on FACodec’s factorized content residual and relies on its pretrained content/prosody/acoustic-detail/timbre separation to isolate accent modification to pronunciation alone.
- Subjective Evaluation — conducts a MUSHRA-like listening test with 13 human participants rating accent similarity across conversion strengths.
- NaturalSpeech 3 — reuses the FACodec factorized codec introduced in this paper as the representation space on which accent conversion is performed, operating specifically on its content residual while keeping prosody, acoustic-detail, and timbre latents fixed.