Voiced-Aware Style Extraction

• Voiced-Aware Style Extraction is a neural approach that targets voiced regions to extract expressive style features, improving speaker identity and prosody modeling.
• It employs frame-level masking, selective encoding, and unvoiced filler modules to isolate and integrate key acoustic details such as pitch and energy.
• Empirical results demonstrate that emphasizing voiced frames reduces pitch error and boosts naturalness and style transfer in modern generative speech systems.

Voiced-aware style extraction refers to neural techniques that selectively extract and model style factors from speech by placing algorithmic emphasis on those acoustic regions—predominantly voiced—that are most informative for expressivity, identity, and prosody. This paradigm, which has evolved from general style or speaker embedding methods, targets the harmonic, energy-rich frames where vocal fold vibration and continuous spectral structure dominate, rather than treating all frames as equal contributors to style. Voiced-aware extraction has demonstrable impact on expressive text-to-speech (TTS), voice conversion, singing synthesis, and related multimodal generation tasks.

1. Principles of Voiced-Aware Style Extraction

The central hypothesis underpinning voiced-aware approaches is that voiced frames in speech or singing—vowels, voiced consonants, and optionally portions of diphthongs—carry the majority of speaker-specific and style-defining characteristics (e.g., emotion, phonation, intensity, intonation), whereas unvoiced regions (fricatives, plosives, silences) offer limited stylistic variation and reduced discriminative power for expressiveness. For instance, Spotlight-TTS directly asserts that "voiced frames... bear most of the speaker’s expressive intent" (Kim et al., 27 May 2025).

To operationalize this principle:

• Voiced frame identification relies on precomputed pitch/energy heuristics or robust detectors (e.g., zero-crossing analysis, pitch trackers).
• Selective encoding applies the primary style extraction pipeline (typically a CNN, RNN, or quantized vector encoder) exclusively or predominantly to the voiced subset of acoustic frames.
• Unvoiced embedding compensation is handled through learnable modules (such as mask code embeddings) or by smoothing mechanisms, to maintain global sequence continuity and avoid artifacts (Kim et al., 27 May 2025).

This focus not only sharpens the information bottleneck for style extraction but also structurally disentangles content and style representations by localizing expressivity modeling to segments most likely to encode it.

2. Model Architectures and Algorithmic Mechanisms

Reference Pathways

A typical voiced-aware style extraction architecture comprises:

• Frame-level Masking: Given a Mel spectrogram $X \in \mathbb{R}^{T \times F}$ and a voiced/unvoiced mask $M_v \in \{0,1\}^T$, voiced content is isolated as $X_v = M_v \circ X$.
• Style Encoder: Processes $X_v$ via a stack of convolutional (or residual vector-quantized, RVQ) encoders, while omitting or minimizing unvoiced positions.
• Unvoiced Filler (UF) Modules: For unvoiced frames, learnable embeddings ("mask-code" vectors) are produced, and multiple UF network blocks (e.g., ConvNeXt + biased attention) blend the voiced and unvoiced segments to reconstruct a full sequence embedding (Kim et al., 27 May 2025).

Quantization and Rotation

Residual vector quantization with the "rotation trick" is employed to ensure quantization preserves both magnitude and angular (directional) relationships in the code space. For each voiced frame embedding $e_t$ with nearest code vector $q_t$,

$$\tilde{q}_t = \mathrm{sg}\left[\frac{\|q_t\|}{\|e_t\|} R(e_t)\right] e_t$$

where $R$ is a Householder reflection, and $\mathrm{sg}[\,\cdot\,]$ indicates stop-gradient for forward pass alignment but gradient-preserving for training updates (Kim et al., 27 May 2025).

Integration in Modern Pipelines

• In Spotlight-TTS, the extracted frame-wise style embedding $E_s$ is integrated into FastSpeech2 by aligning to text frames and linearly projecting into the main decoder path alongside global style vectors (Kim et al., 27 May 2025).
• In "Stylebook" (Lim et al., 2023), content-dependent stylebooks attend to phonetic clusters, making the system natively voiced-aware by linking content and style granularity.

3. Losses and Style Direction Constraints

To enforce that extracted style embeddings are "style-pure"—orthogonal to content but aligned with prosody—voiced-aware extraction leverages tailored geometric objectives:

• Style-disentanglement loss: Promotes orthogonality between content and style vector spaces,

$$L_{sd} = \bigl\|\,\mathrm{sg}[E_c]\,E_s^T\bigr\|_F^2$$

with $E_c$ as content embeddings and $\mathrm{sg}[\,\cdot\,]$ denoting stop-gradient (Kim et al., 27 May 2025).

• Style-preserving loss: Maximizes cosine similarity between projected prosody and style embeddings,

$$L_{sp} = -\sum_{t=1}^{T} \cos\_sim(\tilde{p}_t, \tilde{s}_t)$$

thus explicitly orienting the style representation toward the prosodic manifold.

The combined objective for training voiced-aware TTS systems thus extends standard synthesis and adversarial losses to include:

$$L_{total} = L_{fs2} + \lambda_{rvq} L_{rvq} + \lambda_{adv} L_{adv} + \lambda_{sd} L_{sd} + \lambda_{sp} L_{sp}$$

with empirically chosen multipliers (Kim et al., 27 May 2025, Kim, 18 Nov 2025).

4. Applications Across Modalities

Voiced-aware extraction principles have been instantiated in a range of generative speech pipelines:

• Expressive Text-to-Speech (TTS): Spotlight-TTS employs voiced-aware RVQ encoding with rotation, unvoiced filler modules, and direction adjustments, yielding improved naturalness MOS, style MOS, and expressive prosody transfer over baseline systems (Kim et al., 27 May 2025, Kim, 18 Nov 2025).
• Any-to-Any Voice Conversion: The Stylebook method leverages self-supervised phonetic–style pairs and content-dependent stylebooks whose per-phoneme attention enhances voicing-sensitive style modeling (Lim et al., 2023).
• Singing Voice Synthesis: LAPS-Diff extracts high-level style vectors and explicit pitch contours, enforcing melody and style consistency losses for highly natural vocal outputs in singing contexts (Dhar et al., 7 Jul 2025).
• Zero-Shot Voice Conversion: ECAPA-TDNN-based style encoders combine mel, F0, and energy tracks (with parallel global and local pitch modules) to isolate and transfer voicing and prosodic information across speakers and emotions (Akti et al., 4 Jun 2025).
• Lip Sync/Audiovisual Generation: Audio-aware cross-attention on reference speech dynamically aligns lip motion to style-congruent voiced regions, enabling more faithful subject-specific lip synchronization (Zhong et al., 10 Aug 2024).

5. Experimental Results and Ablative Insights

Systematic ablations consistently demonstrate the necessity and impact of voiced-aware design:

• Omitting voiced-frame selection degrades pitch RMSE and style transfer metrics, e.g., Spotlight-TTS reports RMSE$_{f_0}$ rising from 8.27 Hz (with voiced extraction) to 11.48 Hz (without) (Kim et al., 27 May 2025).
• Removing rotational quantization ("rotation trick") or unvoiced-filler modules measurably decreases expressiveness, increases word error rate, and reduces continuity, verifying the role of both information focus and sequential smoothness.
• Adding or optimizing style–prosody alignment losses further improves naturalness MOS and F1$_{V/UV}$ (voiced/unvoiced discrimination).

A cross-section of metric outcomes is summarized below:

Model/System	nMOS (↑)	sMOS (↑)	WER (↓)	RMSE$_{f_0}$ (Hz, ↓)	F1$_{V/UV}$ (↑)	SECS (↑)
Spotlight-TTS (voiced-aware)	4.26	3.84	12.64%	8.27	0.705	0.906
FS2+GST (baseline)	3.77	3.38	—	9.43	0.687	—

Removing either rotation or unvoiced-filler module deteriorates performance across all style- and prosody-relevant metrics (Kim et al., 27 May 2025).

6. Comparative and Theoretical Context

Distinct from classical style extraction or speaker embedding pipelines, which treat all acoustic regions uniformly, voiced-aware methods impose a learnable or rule-based framewise mask to emphasize where style signals predominantly reside. This approach is complementary to other disentanglement methodologies (e.g., Mix-Style Layer Normalization (Akti et al., 4 Jun 2025), discrete unit bottlenecks, or adversarial losses), and can be combined for further gains in disentanglement, style purity, and cross-linguistic robustness.

A plausible implication is that future synthesis and conversion models will increasingly adopt explicit voiced/unvoiced segmentation as a foundational axis for style modeling, given empirical evidence that style transfer, expressiveness, and speaker similarity all benefit from this targeted approach.

7. Key References and Empirical Validation

• Spotlight-TTS: "Spotlighting the Style via Voiced-Aware Style Extraction and Style Direction Adjustment for Expressive Text-to-Speech" (Kim et al., 27 May 2025, Kim, 18 Nov 2025).
• Stylebook: "Content-Dependent Speaking Style Modeling for Any-to-Any Voice Conversion using Only Speech Data" (Lim et al., 2023).
• LAPS-Diff: "A Diffusion-Based Framework for Singing Voice Synthesis With Language Aware Prosody-Style Guided Learning" (Dhar et al., 7 Jul 2025).
• Non-autoregressive expressive VC: "Towards Better Disentanglement in Non-Autoregressive Zero-Shot Expressive Voice Conversion" (Akti et al., 4 Jun 2025).
• Audio-aware style reference for lip sync: "Style-Preserving Lip Sync via Audio-Aware Style Reference" (Zhong et al., 10 Aug 2024).
• Seminal joint style analysis for TTS: "Interactive Text-to-Speech System via Joint Style Analysis" (Gao et al., 2020).

These works collectively establish the foundational models, algorithmic innovations, and empirical results that define voiced-aware style extraction in modern generative speech and multimodal synthesis systems.