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A Comprehensive Study of Content Representations for Speech Synthesis
Diego Torres, Axel Roebel, Nicolas Obin
TL;DR
The paper addresses the limited common-framework evidence on what speech content representations preserve. It synthesizes speech conditioned only on each representation and evaluates content, speaker identity, and prosody; results span audio-preserving and speaker-disentangling regimes, with disentanglement depending on objective and capacity constraints.
Problem
Speech content representations are rarely compared under a common generative framework that directly measures what they contain, despite their use in multiple speech applications.
Source: §1 · p. 1Method
The paper trains a generative model conditioned solely on each representation and evaluates generated audio for linguistic content, speaker identity, and prosody.
Source: §2 · p. 1, §2.1 · p. 2Results
Representations span regimes from near-reconstruction to speaker-identity masking; supervision alone does not ensure disentanglement, which depends on objective and capacity constraints.
Source: §4 · pp. 3–4Takeaways & Limitations
ASR objectives yield the best speaker disentanglement when constrained by a bottleneck, while codebook-size changes do not shift representations between the two regimes.
Source: §6 · p. 4, §4 · p. 4Generated-audio evaluation is a lower bound on representation information, and objective quality and speaker metrics may miss perceptually relevant distinctions; subjective listening tests remain future work.
Source: §5 · p. 4
Abstract
from arXiv · showhide
Speech content representations are central to voice conversion, speech-to-speech translation, and multimodal language models, yet they are rarely compared under a common generative framework that directly measures what each representation contains. We address this by training a generative model conditioned solely on each representation and evaluating the generated audio along the content, speaker identity, and prosody axes. Across SSL features, supervised tokens, posteriorgrams, and neural audio codecs, we find two distinct regimes: representations that nearly reconstruct the original audio, and representations that effectively disentangle speaker identity. These results show that disentanglement depends not on supervision alone, but on the interaction between the training objective and the representation's information capacity: supervised representations only disentangle speaker identity when their capacity is sufficiently constrained.
1. INTRODUCTION
Speech content representations serve applications with different, sometimes opposing needs for linguistic, speaker, prosodic, and acoustic information. The paper evaluates diverse representations in a shared synthesis framework to assess what they preserve and how their training objectives and information capacity relate to disentanglement.
1. INTRODUCTION
- Different applications need different speech attributes: voice conversion primarily targets timbre, while accent conversion explicitly targets prosody.The relevant question is which aspects a representation captures, and to what extent.Source: §1 · p. 1
- Prior benchmarks rank acoustic and content tokenizers together despite their differing objectives, without directly assessing disentanglement.Other work probes only selected representations or evaluates accent recoverability and voice-conversion architecture choices [15].Source: §1 · p. 1
- The paper uses “content representations” because these features encode phonetic rather than semantic information and need not be discrete.The paper notes that these representations have supported voice conversion, style transfer, speech-to-speech translation, text-to-speech [7], and multimodal models [10].Source: §1 · p. 1
- A shared synthesis framework compares clustering-based representations, supervised tokens, posteriorgrams, and neural audio codecs, revealing regimes shaped by training objective and information capacity.These approaches have rarely been evaluated under one speech synthesis framework.Source: §1 · p. 1
2. METHODOLOGY
The methodology synthesizes speech from each representation without providing speaker identity through another channel, then evaluates what the generated audio preserves. It measures linguistic content, speaker identity, and prosody, with decoder capacity and imperfect metrics limiting interpretation.
2. METHODOLOGY
- Removing speaker IDs and reference audio ensures that speaker information in generated speech must pass through the representation, avoiding an identity-channel confound.Prior setups using those extra inputs can limit interpretation of voice-conversion results as a measure of representation-level disentanglement, without invalidating the results [15].Source: §2 · p. 1
- The model samples speech conditioned on representation r; attributes invariant among compatible utterances are preserved, while varying attributes are resampled.A decoder may fail to recover information that the representation contains, so generated-audio evaluations provide a lower bound on that information.Source: §2 · pp. 1–2
2.1. Evaluation metrics
- Evaluation compares original and generated audio on linguistic content, speaker identity, and prosody using dWER, speaker-verification EER and gender accuracy, and F0-contour error.Content scoring uses only utterances transcribed correctly from real audio to avoid poor-quality audio skewing the distribution; EER of 50% indicates chance verification and no speaker information in the input representation.Source: §2.1 · p. 2
- The prosody error decomposition uses F0-contour means, standard deviations, and correlation; the mean and range differences are called register error and range error.The evaluated F0 contours are measured in cents.Source: §2.1 · p. 2
3. IMPLEMENTATION DETAILS
Experiments use LibriTTS and compare representations at a common 50 Hz frame rate. A shared acoustic model upsamples each representation and generates audio, with evaluation metrics and guidance comparisons specified for the resulting systems.
3.1. Data
- Training and validation use LibriTTS train.clean.100 and train.clean.360, with model selection on dev.clean and final evaluation on test.clean.Synthesis uses 24 kHz audio, resampled to 16 kHz when a self-supervised encoder is involved.Source: §3.1 · p. 2
3.2. Representations
- All representations operate at 50 Hz; the study tests SSL features and their k-means variants, plus a VQ-VAE that reconstructs HuBERT features.HuBERT-base layer 6 is used for subsequent SSL-backbone experiments; the VQ-VAE uses a 256-dimensional codebook.Source: §3.2 · p. 2
- Supervised representations include VQ-ASR with quantized and continuous features, phoneme posteriorgrams and argmax units, and HuBERT-finetuned Soft units.The PPG recognizer predicts 39 ARPAbet phones plus silence; SpeechTokenizer contributes its first RVQ codebook level, while ContentVec uses 100 clusters.Source: §3.2 · p. 2
3.3. Acoustic model
- Evaluation reports content, speaker, and prosody measures, with Table 1 defining metric units and aggregation and Fig. 2 plotting EER and pitch correlation against dWER.Table 3 compares metrics at guidance scales w = 1.0 and w = 2.0.Source: Table 1 · p. 3, Figure 2 · p. 3, Table 3 · p. 3
- A shared acoustic model upsamples representations from 50 Hz to 100 Hz and conditions a flow-matching module to generate 100 Hz melspectrograms.The model has approximately 67M parameters; inference uses a 10-step midpoint solver with guidance scale w = 2.0 and a Vocos vocoder.Source: §3.3 · p. 3
4. RESULTS
The representations span two regimes, from near-reconstruction to effective speaker masking; disentanglement reflects the interaction of supervision and capacity constraints. Codebook size and decoder or guidance changes do not erase the broad distinctions.
4. RESULTS
- VQ-ASR and PPGs effectively mask speaker identity, while raw HuBERT, WavLM, Soft Units, ContentVec, and continuous VQ-ASR remain closer to near-reconstruction.VQ-ASR EER is indistinguishable from chance, and its gender-classification accuracy stays near the 52.9% gender-distribution baseline.Source: §4 · p. 3
4.1. Robustness to decoder capacity and sampling parameters
- With a decoder about three times larger, most metrics show no significant change, and significant differences remain small relative to gaps between representations.The caption identifies Table 4 as the evaluation with a decoder three times as large.Source: Table 4 · p. 4, §4.1 · p. 4
4. RESULTS
- Disentanglement reflects supervision combined with a bottleneck: reconstruction-only VQ-VAE compresses features but achieves little disentanglement.A bottleneck alone is insufficient; the reported interpretation is that supervision and constrained capacity together drive disentanglement.Source: §4 · p. 4
- Smaller codebooks reduce speaker and pitch information, but not enough to move a representation between the two regimes.Within this task framework, VQ-ASR features fit content tokens, whereas VQ-VAE features fit content-style tokens.Source: §4 · p. 4
- For continuous VQ features, reducing dimensionality from 256 to 8 improves disentanglement, with EER rising from 8.6% to 39.7%.The quantized version is largely unaffected by the reduction; dWER and pitch correlation are the only metrics with significant differences.Source: §4 · p. 4
- SpeechTokenizer’s first RVQ level falls between the two regimes, retaining both acoustic and linguistic information through joint reconstruction and HuBERT distillation objectives.At 1024 codes, VQ-VAE performs better as a complete representation, while VQ-ASR and k-means perform better as linguistic representations at comparable or higher EER.Source: §4 · p. 4
- Encoder-level confusion is suggested as the main source of higher dWER for VQ-ASR and PPGs; linguistic content also constrains pitch correlation above zero.Fast or out-of-distribution speech can be assigned incorrect linguistic clusters, yielding plausible but linguistically incorrect audio; PPG pitch correlation is interpreted as a practical lower bound that VQ-ASR approaches.Source: §4 · p. 4
4.1. Robustness to decoder capacity and sampling parameters
- Increasing guidance generally improves dWER and UTMOS without changing representation ordering, apart from a statistically indistinguishable PPGs–k-means 256 pair at w = 1.0.At w = 2.0, disentangled representations score higher on UTMOS than ground-truth Vocos reconstruction; EER is mostly unaffected, aside from a small significant decrease for complete representations.Source: §4.1 · p. 4
5. LIMITATIONS
The framework measures only a lower bound on representation information, and objective audio metrics may miss perceptually relevant distinctions.
5. LIMITATIONS
- The method gives a lower bound on information in each representation, because measuring true information would require a perfect decoder and evaluator.Larger-decoder and guidance checks leave relative ordering largely unchanged, but do not remove this lower-bound qualification.Source: §5 · p. 4
- UTMOS and ECAPA-TDNN are imperfect proxies for human judgment, so subjective listening tests are needed to assess perceptually relevant preservation and discard.The authors leave listening tests to future work.Source: §5 · p. 4
6. CONCLUSIONS
The paper presents a standardized, reference-free generative benchmark and derives tokenization guidelines about supervision, compression, and capacity.
6. CONCLUSIONS
- The standardized, reference-free generative framework benchmarks speech content representations.Source: §6 · p. 4
- ASR objectives yield the best speaker disentanglement only when a bottleneck, such as vector quantization or low-dimensional projection, constrains identity and prosodic leakage.Source: §6 · p. 4
- The conclusions recommend separating compression from content coding, while treating codebook size as secondary to the training objective in setting disentanglement profiles.Jointly optimizing reconstruction and distillation in one token degrades linguistic accuracy and disentanglement relative to dedicated content codes.Source: §6 · p. 4