Source-linked AI summary
Multi-sample Synthetic Supervision for Accent Conversion
Yangyang Qu, Michele Panariello, Massimiliano Todisco, Nicholas Evans
TL;DR
Paired recordings for accent conversion are difficult to collect, while synthetic targets can vary in accent realization and source preservation. The paper selects among multiple stochastic waveform candidates using both properties and transfers selected candidates’ discrete content-style codes to accent-conditioned autoregressive adaptation; eight-sample supervision improves target-accent accuracy with only a small mean WER increase and nearly unchanged speaker similarity.
Problem
Paired recordings of the same speaker saying the same content in different accents are difficult to collect, motivating synthetic supervision and making suitable target construction a central challenge.
Source: §1 · p. 1Method
The framework jointly assesses candidate waveforms for target-accent realization and source preservation, then uses the selected candidates’ paired discrete content-style codes for categorical accent-conditioned autoregressive adaptation.
Source: §1 · p. 1Results
Eight-sample supervision improves target-accent accuracy by 2.49 pp, increases mean WER by 0.29 pp, and changes SIM from 0.7139 to 0.7134; overall, the system has the lowest WER at 7.81% and the highest observed mean listening ratings.
Source: §4.2 · p. 4, §4.1 · p. 4Takeaways & Limitations
At inference, the converter uses the source transcript and categorical target-accent label and requires neither target-accent reference speech nor multi-candidate selection.
Source: §5 · p. 4
Abstract
from arXiv · showhide
Accent conversion (AC) requires changing accent while preserving speaker identity and linguistic content, yet parallel recordings are scarce. Speech synthesis provides an alternative source of supervision, but generated targets vary in accent realization and source preservation. We propose a multi-sample synthetic supervision framework that constructs conversion targets by jointly assessing these properties across candidate waveforms for each source--accent condition. Selected candidates provide associated discrete speech codes, representing linguistic content, prosody, and speaking style, as targets for accent-conditioned autoregressive adaptation. Compared with using one generated target per training example, our method improves target-accent classification accuracy by 2.49 percentage points, while speaker similarity remains nearly unchanged, and word error rate increases by 0.29 percentage points. Across six target accents, our method achieves 7.81% WER and the highest mean listening ratings among evaluated systems.
1. INTRODUCTION
Paired recordings for accent conversion are difficult to collect, making suitable synthetic targets a central challenge. The paper selects among stochastic generations using both accent realization and source preservation, then tests the resulting supervision in a matched comparison.
1. INTRODUCTION
- Synthetic accent-conversion targets must exhibit the requested accent while preserving linguistic content and source-speaker characteristics.The paper therefore treats transcription correctness alone as insufficient for constructing suitable supervision.Source: §1 · p. 1
- Eight-sample supervision improves target-accent accuracy by 2.49 percentage points, with nearly unchanged speaker similarity in the matched comparison.The comparison holds source–accent conditions, data splits, initialization within each seed, and optimization protocol fixed.Source: §1 · p. 1
- The framework jointly assesses multiple stochastic candidate waveforms for target-accent realization and source preservation, then uses a selected candidate’s paired content-style tokens as supervision.These discrete codes represent linguistic content, prosody, and speaking style.Source: §1 · p. 1
- The AC-specific pipeline adapts a pretrained autoregressive backbone to categorical accent control and transfers waveform-level assessment to paired discrete content-style targets for final adaptation.Source: §1 · p. 1
2. METHOD
The method constructs multiple waveform–token candidates for each source–accent condition, selects supervision by assessing candidate waveforms, and trains a categorical autoregressive converter. At inference, the converter generates one token sequence without target-accent reference speech or candidate selection.
2. METHOD
- The autoregressive model is conditioned on transcript t and accent a, while source waveform x is used only for source-timbre conditioning during frozen synthesis.The conversion objective is to change accent while preserving linguistic content and source-speaker identity.Source: §2 · pp. 1–2
2.1.1. Stochastic Candidate Generation
- Reference-conditioned token targets adapt fixed Gϕ to categorical accent control; it then samples N content-style sequences from (t, a), synthesized with x as timbre reference.The tokens represent linguistic content, prosody, and speaking-style information.Source: §2.1.1 · p. 2
- Multiple stochastic waveform–token candidates are generated per source–accent condition, and waveform-level selection determines the token pseudo-target used for final adaptation.Each waveform remains paired with its token sequence, so assessment of the synthesized candidate determines which tokens supervise the final model.Source: §2.1.1 · p. 2, §2.1.2 · p. 2, §2.2 · p. 3
2.1.2. Candidate Assessment and Selection
- Candidates are screened for target-accent evidence, WER, speaker similarity, and duration; feasible pairs rank by accent probability, probability gain, accent hit, speaker similarity, then lower WER.Conditions with no feasible candidate are excluded from final-model training.Source: §2.1.2 · p. 2
2.2. Accent Conversion Model Training
- Final pθ continues from seed-specific categorical warm-starts on selected tokens, optimizing accent embeddings and LoRA parameters while freezing the remaining backbone.The final adaptation minimizes causal token negative log-likelihood without additional accent, content, speaker, or preference losses.Source: §2.2 · p. 3
2.3. Inference
- At inference, pθ generates one token sequence from (t, a); frozen synthesis combines it with x without target-accent reference speech, candidate generation, or candidate selection.The source waveform serves as the source-timbre reference during synthesis, not as an input to the token predictor.Source: §2.3 · p. 3
3. EXPERIMENTAL SETUP
Experiments cover six English target accents using synthetic supervision and a separate test set; systems are compared through shared automatic metrics and a small blinded listening test. The setup also includes a matched single- versus multi-sample comparison.
3.1. Data and Implementation
- The six target accents are Arabic, Chinese, Hindi, Korean, Spanish, and Vietnamese; the 29,754 construction conditions combine 17,649 LibriTTS and 12,105 L2-ARCTIC utterances.The data comprise 235 LibriTTS speakers and 20 L2-ARCTIC speakers, with 4,959 conditions per accent; AC-Eval3K uses 3,000 LibriTTS test-clean utterances from 39 speakers with no speaker or utterance overlap with the construction pool.Source: §3.1 · p. 3
- The fixed categorical generator produces eight candidates per condition, and feasible candidates are partitioned into 15,346 optimization and 807 internal-validation conditions.A separate 300-condition development set is used for checkpoint selection.Source: §3.1 · p. 3
- Candidate selection uses a fixed six-way accent probe, while WER and speaker similarity use Whisper-small and ECAPA-TDNN; the final converter is trained for three seeds.The seeds are 1337, 2027, and 3407.Source: §3.1 · p. 3
3.2. Compared Systems and Evaluation
- External comparisons include five systems that support all six target accents, but their training and inference conditions are not matched to the proposed system.All systems use the same output-level scoring pipeline; target-accent classification accuracy and ∆ptgt measure accent evidence, WER measures content preservation, and SIM measures source-speaker preservation.Source: §3.2 · p. 3
- The matched N = 1 versus N = 8 comparison uses the same 6,600 source–accent conditions, with the first feasible candidate as the N = 1 target and the highest-ranked feasible candidate among eight as the N = 8 target.The comparison holds data split, initialization, and training protocol fixed within each seed; a blinded test with 10 listeners rates 12 utterances on 1–5 naturalness, speaker-similarity, and target-accent-similarity scales.Source: §3.2 · p. 3
4. RESULTS
Across evaluated systems, the proposed system has the lowest WER, while other systems lead on some accent or speaker metrics; its listening-test means are highest on all three measures. In matched training conditions, eight-sample supervision improves target-accent evidence with a small WER increase, and Selected Top-1 is chosen for the final system.
4.1. Overall Conversion Performance
- 7.81% WER is the lowest among evaluated systems; the proposed system has 30.31% target-accent accuracy and ∆ptgt = +11.01 pp, while other systems score higher on some accent metrics and Vevo2 has the highest SIM.The comparison shows different accent–content–speaker operating points rather than one system dominating every metric.Source: §4.1 · p. 4
- In the listening test, the proposed system achieved the highest observed mean ratings for naturalness (N-MOS 3.690), source-speaker similarity (S-MOS 3.560), and target-accent similarity (A-MOS 3.131).Source: §4.1 · p. 4
4.2. Effect of Multi-Sample Supervision
- 54.29% of 29,754 source–accent conditions have at least one feasible candidate among eight generations, compared with 22.18% for the first candidate alone.The increased coverage provides more opportunities to construct usable supervision but does not itself establish a downstream benefit.Source: §4.2 · p. 4
- 2.49 pp higher mean target-accent accuracy and 1.93 pp higher mean ∆ptgt result from N = 8 versus N = 1, alongside a 0.29 pp mean WER increase and SIM changing from 0.7139 to 0.7134.The comparison uses the same 6,600 source–accent conditions; target-accent gains are positive across all three paired training seeds.Source: §4.2 · p. 4
4.3. Alternative Supervision Strategies
- Selected Top-1 has the highest mean target-accent accuracy and ∆ptgt and the lowest mean WER, while Soft Top-2 has the highest mean SIM.Selected Top-1 is used in the final system; the alternatives include random selection near the best candidate and soft-min losses over two candidates or near-best candidates.Source: §4.3 · p. 4, Table 3 · p. 4
5. CONCLUSIONS
The paper studies multi-sample synthetic supervision for accent conversion by selecting among stochastic waveform–token candidates. In controlled comparisons, eight-sample supervision improves probe-measured target-accent accuracy with nearly unchanged speaker similarity, while the final system has the lowest WER and highest observed mean ratings among evaluated systems.
5. CONCLUSIONS
- The framework selects among stochastic waveform–token realizations of a source–accent condition, then uses the selected token sequence for final autoregressive adaptation.At inference, the converter requires source transcript and categorical target-accent label, but not target-accent reference speech or multi-candidate selection.Source: §5 · p. 4
- Eight-sample supervision improves probe-measured target-accent accuracy by 2.49 pp, with a 0.29-pp increase in mean WER and nearly unchanged speaker similarity.Source: §5 · p. 4
- The final system achieves the lowest WER and highest observed mean ratings on all three perceptual measures among evaluated systems, in an evaluation limited to six target accents and external systems with unmatched training and inference conditions.The paper identifies broader accent settings and independent evaluation of the matched multi-sample effect as future work.Source: §5 · p. 4