# Lina-Style: Word-Level Style Control in TTS via Interleaved Synthetic Data

Théodor Lemerle, Nicolas Obin, Axel Roebel

► To cite this version:

Théodor Lemerle, Nicolas Obin, Axel Roebel. Lina-Style: Word-Level Style Control in TTS via Interleaved Synthetic Data. 13th edition of the Speech Synthesis Workshop, Aug 2025, Leeuwarden, Netherlands. pp.35-39, 10.21437/SSW.2025-6 . hal-05498178

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# Lina-Style: Word-Level Style Control in TTS via Interleaved Synthetic Data

Théodor Lemerle, Nicolas Obin, Axel Roebel

STMS Lab, IRCAM - Sorbonne Université - CNRS, Paris, France

{lemerle, obin, roebel}@ircam.fr

## Abstract

We propose a method for word-level style conditioning in text-to-speech (TTS) based on synthetic data, enabling emotion and style control with limited supervision. We first train a TTS model on stylistically unlabeled data. Then, using that base model, we synthesize multiple stylistic renditions of the same sentences by conditioning on expressive samples from a small labeled corpus. Using cross-attention alignments, we interleave segments from different styles to construct synthetic examples with local style variation. To provide independent control of style intensity, we generate samples with classifier-free guidance at varying strengths and condition the model accordingly. This fully synthetic parallel dataset allows the model to learn precise and coherent control at the word level. Despite relying solely on synthetic supervision, our approach performs similarly to fine-tuned baselines while offering greater controllability. We release code and weights at https://github.com/theodorblackbird/lina-style.

**Index Terms:** emotional text-to-speech, emotion control

## 1. Introduction

Text-to-speech (TTS) systems have made impressive strides in producing natural and intelligible speech. However, controlling expressive dimensions such as emotion or speaking style—especially at fine-grained temporal resolutions—remains a significant challenge.

Recent advances in large-scale TTS models have focused on scaling both model capacity and training data [1, 2], often leveraging tens of thousands of hours of weakly labeled speech. This scaling has given rise to in-context learning capabilities, enabling zero-shot voice cloning via prompt-based continuation or infilling. Yet, these models still struggle to control precise speech attributes such as prosody and emotion. This is mainly due to the scarcity of stylistically annotated data—particularly at the word or sub-sentence level—which hinders direct conditioning approaches that rely on well-curated (text, speech, style) triplets.

In this work, we propose a synthetic data approach to enable word-level style control in TTS using only a small set of high-quality expressive data. By synthesizing multiple stylistic versions of a sentence with an expressive teacher model, we construct a synthetic parallel corpus.

We then interleave the stylistic variants using alignment information from the teacher's cross-attention weights, and fine-tune the teacher model to associate style tokens with localized variations of emotion or style.

<figure><img src="image_2.png" /><figcaption>"[Happy=0.7] We were supposed to meet at seven,
[Confused=1.3] but no one showed up, [Sad=1.8] and I waited alone in the rain."</figcaption></figure>

<caption>Figure 1: A base Lina-Speech model is fine-tuned on its own outputs, conditioned with style tokens that are appended to the text-token vocabulary and modulated by a single linear layer that adapts the intensity of each style token.</caption>

### 1.1. Related work

Emotion and style control for TTS has been explored through style tokens [3], reference encoders [4] or textual descriptions [5, 6, 7]. EmoDiff [8] proposes a classifier guidance approach for intensity modulation of emotion in speech. However, these approaches require dense labeling and can only condition the model at the utterance level. Recently, CosyVoice2 [9] introduced word-level emphasis and para-lingual control based on a large-scale annotated dataset. EmoKnob [10] introduces a training-free framework for fine-grained emotion control in speech synthesis, enabling few-shot transfer and open-ended text-based emotion specification by manipulating speaker embeddings in a large-scale pre-trained TTS model. Our work relates to synthetic dataset generation [11, 12] techniques in general.

### 1.2. Contributions

This work makes the following contributions:

* **Word-Level emotion and style conditioning without fine-grained Labels:** We propose a method for achieving fine-grained, word-level control of speaking style in TTS without requiring ground-truth word-level style annotations.

* **Interleaving via cross-attention alignments:** We synthesize parallel stylistic renditions of the same sentence and interleave word-level segments using cross-attention weights from the synthetic samples, eliminating the need for external forced aligners.

* **Style intensity distillation through Classifier-Free Guidance:** By modulating generation strength via classifier-free guidance and conditioning the model on the corresponding
<figure><img src="image_3.png" /><figcaption>Figure 2: High-level overview of Lina-Speech [13], the base TTS model used for synthetic data generation. It comprises a neural codec language model with an encoder-decoder architecture. Audio modeling is performed using a GLA-based causal transformer, while text encoding is handled by a non-causal self-attention transformer. Crucially, our approach relies on the materialization of the cross-attention weights between text and audio.</figcaption></figure>

strength level, we enable precise control over style intensity. A welcome side effect of this approach is that it avoids the need to double the batch size, as typically required when using CFG.

## 1.3. Lina-Speech

Our approach builds upon recent advances in expressive TTS modeling, particularly Lina-Speech [13], which proposes an alternative to self-attention transformer-based autoregressive TTS models. Lina-Speech proposes Gated Linear Attention[14] (GLA) as a principled replacement for self-attention, significantly improving inference throughput, especially for long-form or batched generation. This property is particularly appealing in our setting, where generating large amounts of synthetic data is a core component. Furthermore, Lina-Speech supports prefix-free prompting through initial-state tuning [15], a technique that enables prompting speaker or style information directly into the model's initial recurrent state. This method has been shown to be efficient in prompting emotion and style conditioning without the need for context-heavy prompts and supports arbitrary numbers of samples. In our work, we take advantage of these properties to efficiently generate high-quality, stylistically varied utterances, which serve as the foundation for our word-level synthetic augmentation method. The model architecture consists of a speech codec language model with a text encoder, an audio decoder and cross-attentions layers between the two. The audio decoder replaces traditional self-attention with Gated Linear Attention, while the text encoder is composed of non-causal self-attention.

## 1.4. Initial-State Tuning

Initial-state tuning [13, 15, 16] is a parameter-efficient fine-tuning method used in Lina-Speech to condition the model on speaker or style information, belonging to the family of soft-prompting. Rather than prepending a prompt or using a speaker embedding, this technique initializes the model's recurrent state with a learnable matrix that is tuned on a small set of samples, while keeping all other model parameters frozen. This approach

is motivated by the fact that GLA can be written as a linear RNN with matrix state $S_t$ where $q_t$, $k_t$ and $v_t$ are different linear projections of the input sequence:

$$S_t = G_t \odot S_{t-1} + k_t^\top v_t, \quad o_t = q_t S_t. \quad (1)$$

By default the initial-state $S_0$ is initialized as 0 but it can be tuned a posteriori and it has been shown to be especially effective for fast adaptation, multi-sample prompting and emotive speech generation in TTS [13]. In practice, $S_0$ is parameterized and optimized as a learnable rank-one matrix for regularization purposes:

$$S_0 = k_0^\top v_0.$$

## 1.5. Classifier-Free Guidance (CFG) [17]

Classifier-Free Guidance (CFG) [17] is a simple yet powerful technique originally introduced for conditional generation in diffusion models. It improves the effectiveness of conditioning at inference time by linearly combining the model's unconditional and conditional outputs.

In our context, let $f(x|c)$ denote the predicted logits of the model conditioned on a style label $c$ (e.g., "happy", "sad"), and let $f(x)$ represent the unconditional output (obtained by disabling conditioning, i.e. setting $S_0 = 0$). The final output is computed as:

$$f_{\text{CFG}}(x|c) = f(x) + \alpha \cdot (f(x|c) - f(x))$$

where $\alpha$ is the guidance scale that controls the influence of the conditioning. When $\alpha = 0$, the model generates unconditionally, and as $\alpha \to \infty$, the output is increasingly biased toward the conditional distribution.

# 2. Lina-Style

## 2.1. Method

We consider two datasets, one large-scale stylistically unlabeled and one small-scale with a style label for each excerpt. Our method consists of the following pipeline:

1. **Base model:** We train a base Lina-Speech model on the large-scale dataset without any style or speaker conditioning.

2. **Multi-style synthesis:** We synthesize multiple stylistic (e.g., neutral, happy, enunciated, confused...) renditions of the same transcript using the base model conditioned on the small style-annotated dataset using initial-state tuning on each speaker and style (see Figure 3a).

3. **CFG distillation:** To capture style intensity, we employ CFG with uniformly random guidance strength between 0.5 and 2.

4. **Token-level alignment:** We rely on the cross-attention weights from the base model to align the different synthetic versions and extract word-level boundaries. We employ Monotonic-Alignment Search [18, 19] for discretizing the soft alignments into hard ones before fine-tuning ( contrary to [19] and others [20, 21, 22], we do not use it during training but solely offline).

5. **Interleaving stylistic segments:** Using the alignments, we interleave segments from different styles at word boundaries to construct synthetic utterances with local style variation (see Figure 3b).

6. **Style token supervision:** We insert style tokens at interleaving points in the text transcript to provide localized conditioning signals and guide the model toward word-level expressivity. The style tokens are modulated with a single linear layer
<figure><img src="image_4.png" /><figcaption>Figure 3: Interleaved, word-level annotated synthetic data.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 4: Token-level alignments are derived from the cross-attention weights (top) using MAS [18, 19] (bottom).</figcaption></figure>

that takes as input the strength $\alpha$ of the guidance concatenated to the style token.

7. **Boundary masking:** To prevent the fine-tuning to learn artifacts caused by mismatched transitions (e.g., pops, glitches), we mask tokens at interleaving boundaries during fine-tuning.

8. **Fine-Tuning:** We fine-tune the base model on the resulting synthetic dataset.

# 3. Experiments

## 3.1. Dataset

We use the English split of the MLS [23] dataset and the three training splits from LibriTTS[24, 25], comprising 12k hours of stylistically unlabeled speech for pre-training. For state tuning on emotion and style, we use the Expresso dataset [26] which is composed of 4 speakers and which provides approximately 13 minutes of annotated data per speaker and style. We discard the samples that contain para-lingual instructions. The considered styles are: *whisper*, *enunciated*, *happy*, *sad*, and *confused*. We use WavTokenizer-medium [27] with a token rate of 75Hz as our speech codec.

## 3.2. Pre-Training

The base model is trained mostly following the setup described in [13] and consists of a 330M parameters encoder-decoder with base dimension 1280, 16 layers of GLA transformers audio decoder and 6 layers of self-attention text encoder. We train for 500k steps with a batch size of approximately 100k tokens. We use AdamW with a learning rate of $2 \times 10^{-4}$, a cosine decay schedule with linear warmup during the first 1k steps, weight decay of 0.1, and gradient clipping of 1. We bucket samples by length into 10 groups to improve batching efficiency.

## 3.3. Initial-State Tuning

We optimize an initial-state matrix for each (speaker, style) pair. Tuning is performed over 200 steps using AdamW with a learning rate of 0.1, a rank-one initial state $S_0$, a dropout rate of 0.5, and a batch size of 8. This process takes less than 30 seconds per style on a single GPU. The resulting tuned state is then reused for all subsequent generations.

## 3.4. CFG distillation

To maximize the quality of the synthetic dataset, we generate the text transcripts from the clean 360-hour training split from LibriTTS speech (which has been seen during the training of the base model) for each considered style and speaker in the Expresso dataset. During synthesis, we randomly sample a classifier-free guidance (CFG) strength from a uniform distribution between 0.5 and 2.0.

To ensure the quality of the synthetic data, we run whisper-large-v3-turbo on the generated utterances and filter out those with a character error rate (CER) greater than 10%. We have found this filtering step important, as higher CFG strength values may lead to degraded or unintelligible speech synthesis.

For the sake of evaluation, we linearly discretize the CFG strength into five bins representing increasing levels of guidance. During fine-tuning, each intensity bin is represented by a learnable embedding, and each intensity embedding is concatenated with its corresponding style token and the fed to a single linear layer. The resulting style-intensity embedding is then placed among the text embeddings as illustrated in Figure 1.

## 3.5. Fine-Tuning

We fine-tune the generated dataset for 100,000 steps, using a batch size of 64 and the same hyperparameters as in pre-training. We concatenate the synthetic and interleaved datasets with the original clean training split of LibriTTS. During fine-
tuning, we mask 16 tokens in the surrounding of each boundary (approximately 200 ms around each boundary).

## 3.6. Evaluation of speech synthesis

We assess the overall speech quality of our system by comparing it to two baselines: Parler-TTS Mini [6], fine-tuned on Expresso, and the teacher model Lina-Speech [13]. As these baselines do not support word-level conditioning, we adapt our evaluation setup accordingly:

1. We select a test set from *Expresso* and augment each sample using a large language model to embed the original utterance within a longer sentence.

2. We synthesize the augmented sentence using *Lina-Style*, conditioning only on the original (non-augmented) segment. We use the 3rd intensity bin, corresponding to a CFG strength centered around 1.25, we use the same strength for *Lina-Speech*.

3. We extract and compare only the conditioned segment with the outputs of the baseline models, aligning and cropping them based on cross-attention maps.

Speaker conditioning is achieved by providing a 3s prompt from the "default" style following common practices in codec language models [2, 28]. We conducted a 5-point mean opinion score (MOS) evaluation on the platform Prolific, where 85 listeners rated 15 samples along two dimensions: naturalness (NMOS, i.e., human-likeness of the speech) and speaker similarity (SMOS, i.e., similarity to the speaker identity and intended style) relative to reference recordings from the same speaker and style. We also assessed the perceptual impact of the audio tokenizer we employ.

The results, summarized in Table 1, show that Parler-TTS [6] achieves the highest naturalness scores. However, it is important to contextualize this performance: the released checkpoint was trained on the full Expresso dataset which contains our evaluation samples, has nearly twice as many parameters as our model, and uses DAC [29], a neural codec that encodes speech at a significantly higher bitrate. In contrast, our system employs a more compact speech tokenizer, which impacts both naturalness and similarity. However it underperforms Lina-Speech and Lina-Style in terms of similarity, as both initial-state tuning and our synthetic augmentation method tend to provide a stronger conditioning signal.

Despite these differences, Lina-Style performs comparably to its teacher, Lina-Speech, in terms of both naturalness and speaker similarity, while offering fine-grained, word-level control.

## 3.7. Intensity Control Pairwise Comparison

We evaluate the effectiveness of intensity control in our data-distillation approach and compare it to the base Lina-Speech model using CFG. For practical feasibility, we restrict the evaluation to three intensity levels: *low*, *medium*, and *high*, corresponding to the 1st, 3rd, and 5th intensity bins defined in Section 3.4.

To assess perceived differences in intensity, we perform a pairwise perceptual evaluation. We group synthesis by pairs of different intensity and ask the listener "Which sample sounds more intensely [style]?". The possible answers are either one of the two samples or "Cannot decide". We report the results in Figure 5. We found comparable results for the teacher Lina-Speech and the student Lina-Style, suggesting that the distillation of the CFG keeps most of the control ability of its teacher.

We asked 45 listeners on the platform Prolific, each participant rating 15 pairs.

<figure><img src="image_6.png" /><figcaption>Figure 5: Pairwise comparison results. Lina-Style shows comparable perceived intensity control with its teacher model, Lina-Speech.</figcaption></figure>

<table><caption>Table 1: Mean MOS, SMOS with 95 % confidence interval, and CER (Whisper).</caption><thead><tr><th>Model</th><th>MOS</th><th>SMOS</th><th>CER</th></tr></thead><tbody><tr><td>GT</td><td>4.00 ± 0.14</td><td>3.89 ± 0.20</td><td>3.2</td></tr><tr><td>GT (WavTokenizer [27]})</td><td>3.75 ± 0.17</td><td>3.64 ± 0.20</td><td>3.2</td></tr><tr><td>Parler-TTS Mini [6]</td><td><b>3.67</b> ± <b>0.13</b></td><td>3.04 ± 0.19</td><td>7.3</td></tr><tr><td>Lina-Speech [13]</td><td>3.27 ± 0.16</td><td><b>3.22</b> ± <b>0.20</b></td><td>6.2</td></tr><tr><td>Lina-Style (ours)</td><td>3.26 ± 0.17</td><td><b>3.24</b> ± <b>0.18</b></td><td>6.5</td></tr></tbody></table>

## 4. Limitations and future work.

Although we found our approach successful using only limited labeled data, we found the generalization to an unseen speaker to be limited. Also, we rely crucially on large enough pretraining as it is necessary for the state-tuning and training on synthetic data stage, which might prevent this approach from being applicable for low-resource languages. Future work may explore approaches that enables disentanglement of the attributes for better generalization to more speaker and multilingual pretraining for transferring prompting abilities.

## 5. Conclusion

We introduced *Lina-Style*, a synthetic data augmentation approach for word-level style control in TTS through interleaved
synthetic data. Our approach enables fine-grained expressive control without requiring dense word-level style annotations, leveraging only a small set of labeled expressive data. By synthesizing stylistic variants with a strong teacher model and interleaving them based on cross-attention alignments, we construct a fully synthetic parallel corpus annotated with both style and intensity at the word level. Our results highlight the potential of synthetic data for fine-control of TTS models.

# 6. Acknowledgments

This research was supported by the project EXOVOICES ANR-21-CE23-0040 and funded by the French National Research Agency. This work was granted access to the HPC resources of IDRIS under the allocation 2025-AD011015461 made by GENCI.

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