Advancing Zero-shot Text-to-Speech Intelligibility across Diverse Domains via Preference Alignment
Authors: Xueyao Zhang, Yuancheng Wang, Chaoren Wang, Ziniu Li, Zhuo Chen, Zhizheng Wu
Accepted at: acl25

Advancing Zero-shot Text-to-Speech Intelligibility across Diverse Domains via Preference Alignment

Xueyao Zhang*1, Yuancheng Wang*1, Chaoren Wang1, Ziniu Li1, Zhuo Chen2, Zhizheng Wu1
1The Chinese University of Hong Kong, Shenzhen
2ByteDance Seed

Abstract

Modern zero-shot text-to-speech (TTS) systems, despite using extensive pre-training, often struggle in challenging scenarios such as tongue twisters, repeated words, code-switching, and cross-lingual synthesis, leading to intelligibility issues. To address these limitations, this paper leverages preference alignment techniques, which enable targeted construction of out-of-pretraining-distribution data to enhance performance. We introduce a new dataset, named the Intelligibility Preference Speech Dataset (INTP), and extend the Direct Preference Optimization (DPO) framework to accommodate diverse TTS architectures. After INTP alignment, in addition to intelligibility, we observe overall improvements including naturalness, similarity, and audio quality for multiple TTS models across diverse domains. Based on that, we also verify the weak-to-strong generalization ability of INTP for more intelligible models such as CosyVoice 2 and Ints. Moreover, we showcase the potential for further improvements through iterative alignment based on Ints. Audio samples are available at https://intalign.github.io/.

1 Introduction

Despite leveraging large-scale pre-training (Anastassiu et al., 2024; Wang et al., 2025a; Du et al., 2024b), modern zero-shot TTS systems still lack robustness during real-world applications (Sahoo et al., 2024; Neekhara et al., 2024). These systems struggle to meet even the most fundamental requirement of speech synthesis – intelligibility (Tan, 2023) in several scenarios, including: (1) the target text is hard to pronounce, such as tongue twisters or continuously repeated words (Neekhara et al., 2024; Anastassiu et al., 2024), which is referred to as articulatory cases in this paper, (2) code-switching cases, where the target text contains a mixture of multiple languages, and (3) cross-lingual cases, where the languages of the target text and the reference speech differ. In these domains, existing zero-shot TTS models frequently exhibit “hallucination” issues, such as content insertion, omission, and mispronunciation (Neekhara et al., 2024; Wang et al., 2023).

We attribute these intelligibility challenges primarily to the problem of out-of-distribution (OOD). For example, in cross-lingual cases, there exists a huge mismatch between monolingual pre-training and cross-lingual inference. While including such scenarios in pre-training data would be a natural solution, collecting high-quality data for challenging cases like cross-lingual synthesis remains difficult.

Motivated by the above, we propose to use preference alignment (PA) (Ouyang et al., 2022; Bai et al., 2022) to mitigate the OOD issues, and thus enhance zero-shot TTS intelligibility. The potential of this approach lies in two aspects. First, PA’s customized post-training on human expected distribution can effectively mitigate the OOD issue (Zhang et al., 2024b; Li et al., 2024a; Xiong et al., 2024). Second, unlike TTS pre-training that requires high-quality supervised data, PA needs only paired samples with relative preferences – notably, even synthetic data can lead to large improvements (Dubey et al., 2024; Yang et al., 2024b), thus significantly simplifying data collection for challenging scenarios like cross-lingual cases. Centered on this direction, we investigate three research problems:

• P1: Dataset quality is crucial for model performance. To construct a high-quality intelligibility preference dataset, what prompts and base models should be selected, and how can we establish human-aligned preference pairs?

• P2: Unlike textual LLMs with predominantly autoregressive (AR) design, zero-shot TTS models employ diverse architectures, including AR-based (Borsos et al., 2023a; Anastassiu et al.,
2024; Du et al., 2024b), Flow-Matching (FM) based (Le et al., 2023; Eskimez et al., 2024; Chen et al., 2024c), and Masked Generative Model (MGM) based (Ju et al., 2024; Wang et al., 2025a). How can we design alignment algorithms for various architectures?

• P3: Can our preference dataset demonstrate weak-to-strong generalization (Burns et al., 2024)? In other words, can datasets created using less capable models effectively train more powerful models? This question is central to understanding the scalability and transferability of our dataset design.

In this paper, we address the aforementioned problems with the following key contributions:
→ P1: We establish a synthetic Intelligibility Preference Speech Dataset (INTP), comprising about 250K preference pairs (over 2K hours) of diverse domains. Specifically, INTP covers multiple scenarios, utilizing various TTS models for data creation. Besides, we employ several strategies to construct preference pairs, aiming to mitigate the risk of reward hacking for simple patterns (Skalse et al., 2022; Weng, 2024). Particularly, we leverage human knowledge and DeepSeek-V3 (DeepSeek-AI et al., 2024) to introduce perturbations into TTS systems, creating human-guided negative samples. In addition, when using Word Error Rate (WER) to determine intelligibility preferences, we not only consider self-comparison within a single model as in previous studies (Tian et al., 2024; Yao et al., 2025; Hussain et al., 2025), but also introduce comparisons across different models to leverage their complementary capabilities.
→ P2: We adopt the idea of Direct Preference Optimization (DPO) (Rafailov et al., 2023) to enhance various zero-shot TTS architectures. We employ the vanilla DPO algorithm for AR-based TTS models, while proposing extended versions of it for FM-based and MGM-based models. Our experiments on INTP shows that these algorithms effectively improve the intelligibility, naturalness, and overall quality of multiple state-of-the-art TTS systems, including ARS (AR-based) (Wang et al., 2025a), F5-TTS (FM-based) (Chen et al., 2024c), and MaskGCT (MGM-based) (Wang et al., 2025a).
→ P3: To investigate INTP’s weak-to-strong generalization capability (Burns et al., 2024) on more powerful base models, we research its alignment effects on CosyVoice 2 (Du et al., 2024b) and Ints (Appendix C). Both models are initialized from textual LLMs (CosyVoice 2: from Owen2.5, 0.5B (Yang et al., 2024a). Ints: from Phi-3.5-mini-instruct, 3.8B (Abdin et al., 2024)) and achieve superior intelligibility performance (Table 4). Our experimental results verify that INTP, though constructed from weaker models, remains effective for these two strong models. Additionally, we showcase how to establish an iterative preference alignment “flywheel” of data and model improvements (Bai et al., 2022; Dubey et al., 2024; Xiong et al., 2024) based on Ints.

We will open-source all resources used in this study at Amphion1 (Zhang et al., 2024c), including: (1) the proposed INTP and DPO-based alignment codebase for various TTS models, (2) all the INTP-enhanced models based on Ints, CosyVoice 2, ARS, F5-TTS, and MaskGCT, and (3) our newly constructed zero-shot TTS evaluation sets across diverse domains.

2 Related Work

Zero-Shot Text to Speech Given a target text and a reference speech as input, zero-shot TTS systems aim to synthesize the target text while mimicking the reference style. Modern zero-shot TTS systems include AR approaches (Wang et al., 2023; Peng et al., 2024; Anastassiou et al., 2024; Guo et al., 2024; Du et al., 2024a,b; Zhang et al., 2025) that model discrete speech tokens (Zeghidour et al., 2021; Défossé et al., 2023), and Non-AR approaches that either model continuous representations using diffusion (Shen et al., 2024) or flow matching (Le et al., 2023; Eskimez et al., 2024; Chen et al., 2024c), or model discrete tokens using masked generative models (Borsos et al., 2023b; Ju et al., 2024; Wang et al., 2025a,b). While these systems, trained on large-scale datasets (He et al., 2024; Kahn et al., 2020; He et al., 2025), show excellent intelligibility in regular cases (Anastassiou et al., 2024; Panayotov et al., 2015; Du et al., 2024b), they still struggle with intelligibility in real-world scenarios.

Alignment for Speech Generation Alignment via post-training has demonstrated its effectiveness in the generation of text (Ouyang et al., 2022; Bai et al., 2022), vision (Xu et al., 2023; Fu et al., 2024), speech (Zhang et al., 2024a; Anastassiou et al., 2024; Du et al., 2024b), music (Cideron et al., 2024), and sound effects (Majumder et al., 2024; Liao et al., 2024). In speech generation, existing

1https://github.com/open-mmlab/Amphion
<table>
  <tr>
    <th></th>
    <th>Regular</th>
    <th>Repeated</th>
    <th>Code-Switching</th>
    <th>Pronunciation-perturbed</th>
    <th>Punctuation-perturbed</th>
    <th>#Total</th>
  </tr>
  <tr>
    <td>ARS (Wang et al., 2023a)</td>
    <td>8,219</td>
    <td>8,852</td>
    <td>8,300</td>
    <td>7,225</td>
    <td>8,036</td>
    <td>40,732</td>
  </tr>
  <tr>
    <td>FS-TTS (Chen et al., 2024c)</td>
    <td>8,425</td>
    <td>8,555</td>
    <td>7,976</td>
    <td>7,909</td>
    <td>6,667</td>
    <td>39,532</td>
  </tr>
  <tr>
    <td>MaskGCT (Wang et al., 2025a)</td>
    <td>9,055</td>
    <td>10,263</td>
    <td>8,289</td>
    <td>7,604</td>
    <td>7,686</td>
    <td>42,897</td>
  </tr>
  <tr>
    <td>Intra Pairs</td>
    <td>25,699</td>
    <td>27,670</td>
    <td>24,565</td>
    <td>22,838</td>
    <td>22,389</td>
    <td>123,161</td>
  </tr>
  <tr>
    <td>Inter Pairs</td>
    <td>27,008</td>
    <td>27,676</td>
    <td>24,651</td>
    <td>25,045</td>
    <td>23,970</td>
    <td>128,350</td>
  </tr>
  <tr>
    <th>#Total</th>
    <th>52,707</th>
    <th>55,346</th>
    <th>49,216</th>
    <th>47,883</th>
    <th>46,359</th>
    <th>251,511</th>
  </tr>
</table>

(a) Distribution of preference pairs, where pronunciation-perturbed and punctuation-perturbed texts are introduced to create the human-guided negative samples.

<table>
  <tr>
    <th>Text Type</th>
    <th>Example</th>
  </tr>
  <tr>
    <td>Regular</td>
    <td>A panda eats shoots and leaves.</td>
  </tr>
  <tr>
    <td>Repeated</td>
    <td>A panda pands eats shoots and leaves and leaves and leaves.</td>
  </tr>
  <tr>
    <td>Code-Switching</td>
    <td>熊猫吃，shoots 和 leaves</td>
  </tr>
  <tr>
    <td>Pronunciation-perturbed</td>
    <td>A pan duh eight shoots n leafs.</td>
  </tr>
  <tr>
    <td>Punctuation-perturbed</td>
    <td>A panda eats, shoots, and leaves.</td>
  </tr>
</table>

(b) Examples of different types for a text, “A panda eats shoots and leaves”.

Table 1: Intelligibility Preference dataset (INTP). There are about 250K pairs (over 2K hours) in INTP, covering various texts and speeches, multiple models, and diverse preference pairs.

works have employed preference alignment to enhance multiple aspects of speech, including intelligibility (Anastassiou et al., 2024; Du et al., 2024b; Tian et al., 2024), speaker similarity (Anastassiou et al., 2024; Du et al., 2024b; Tian et al., 2024), emotion controllability (Anastassiou et al., 2024; Gao et al., 2024), and overall quality (Zhang et al., 2024a; Chen et al., 2024a; Hu et al., 2024; Chen et al., 2024b; Yao et al., 2025; Hussain et al., 2025). For intelligibility, previous studies choose WER as the optimization objective, either directly employing it as a reward model (Anastassiou et al., 2024; Du et al., 2024b) or centering around it to construct preference pairs (Tian et al., 2024; Yao et al., 2025; Hussain et al., 2025).

However, the existing research exhibits two main limitations. First, in constructing intelligibility preference dataset, current works rely solely on a single model to generate data (Tian et al., 2024; Yao et al., 2025; Hussain et al., 2025), neglecting comparisons across different models. Additionally, beyond the objective WER, the potential of leveraging human knowledge or feedback to construct preference pairs remains unexplored. Second, most existing work has focused primarily on optimizing AR-based (Zhang et al., 2024a; Anastassiou et al., 2024; Du et al., 2024b; Tian et al., 2024) or diffusion-based (Chen et al., 2024b) TTS models, leaving open the question of how to design effective alignment algorithms for other architectural paradigms, such as FM-based and MGM-based TTS models.

3 INTP: Intelligibility Preference Speech Dataset

To enhance the TTS intelligibility, this study opts for constructing a preference dataset to align (Tian et al., 2024; Yao et al., 2025; Hussain et al., 2025) rather than directly optimizing single metrics or rules such as WER (Anastassiou et al., 2024; Du et al., 2024b). This choice is motivated by two key considerations. First, through the construction of a preference dataset, we can inject human knowledge and feedback beyond WER, such as creating human-guided negative samples in the framework of preference alignment (Section 3.3). Second, in addition to the existing approach of constructing preference pairs from multiple samples of a single model (Tian et al., 2024; Yao et al., 2025; Hussain et al., 2025), we can leverage comparisons across different models to create preference pairs, thereby utilizing the complementary capabilities of various models (Figure 1b). These different strategies help increase diversity in the dataset, mitigating the risk of “reward hacking” that often results from the simple patterns inherent in single metrics or rules (Bai et al., 2022; Skalse et al., 2022; Weng, 2024).

Formally, we aim to construct an intelligibility preference dataset \( \mathcal{D} = \{(x, y^w, y^l)\} \), where each triplet comprises a prompt \( x \) (consisting of target text \( x^{text} \) and reference speech \( x^{speech} \) for zero-shot TTS models), along with a pair of synthesized speech samples (\( y^w, y^l \)). Here, \( y^w \) and \( y^l \) represent the preferred (positive) and dispreferred (negative) outputs conditioned on \( x \), respectively. Statistics of the proposed INTP are presented in Table 1.

3.1 Prompt Construction

To establish a high-quality preference dataset, we aim to make the distribution of prompt \( x \) cover a wide range of domains. For the target text \( x_{text} \), from the linguistic perspective, we design three distinct categories: (1) Regular text, which represents the general cases for TTS systems, aimed at enhancing model intelligibility in common scenarios; (2) Repeated text, which contains repeated or redundant words and phrases, specifically designed to improve TTS performance in articulatory cases; and (3) Code-switching text, which incorporates a mixture of different languages, intended to en-
hance TTS capabilities in multilingual scenarios. From the semantic perspective, we collect text content across diverse topics and domains to enrich the distribution of \( x^{text} \). For the reference speech \( x^{speech} \), we aim to cover a wide range of speakers, speaking styles, and acoustic environments. Regarding the pairing of \( x^{text} \) and \( x^{speech} \), we further consider their language alignment by constructing both monolingual and cross-lingual combinations (more statistics in Appendix A.1).

We construct these prompt data based on the Emilia-Large (He et al., 2024, 2025), which contains real-world speech data and textual transcriptions across diverse topics, scenarios, and speaker styles. We perform stratified sampling on Emilia-Large’s speech and text data to obtain multilingual prompts. We employ DeepSeek-V3 (DeepSeek-AI et al., 2024) to preprocess the sampled text, including typo correction, and use it as regular text. Based on these regular texts, we further utilize DeepSeek-V3 to transform them into different text types (as shown in Table 1b). Construction details are provided in Appendix A.1.

3.2 Model Selection

We utilize multiple zero-shot TTS models with diverse architectures for data synthesis to enhance INTP’s diversity and generalization. Specifically, we select the following three models: (1) ARS (AR-based): Introduced as an autoregressive baseline by Wang et al. (2025a), and referred to as “AR + SoundStorm” in the original paper (Wang et al., 2025a). It adopts a cascaded architecture, including the autoregressive text-to-codec and the non-autoregressive codec-to-waveform (Borsos et al., 2023b). (2) F5-TTS (FM-based): It follows E2 TTS (Eskimez et al., 2024) and uses a flow-matching transformer (Le et al., 2023; Lipman et al., 2023) to convert the text to acoustic features directly (Chen et al., 2024c). (3) MaskGCT (MGM-based): Similar to ARS, MaskGCT employs a two-stage architecture. The key distinction lies in its use of an MGM in the text-to-codec stage (Wang et al., 2025a).

All the three are pre-trained on Emilia (He et al., 2024) (about 100K hours of multilingual data) and represent state-of-the-art zero-shot TTS systems across different architectures. We utilize their officially released pre-trained models (see Appendix A.2 for details) to generate data for INTP.

3.3 Preference Pairs Construction

In constructing intelligibility preference pairs, we design three categories of pairs (Figure 1):

Intra Pair These pairs are generated through model self-comparison (Figure 1a), following an approach similar to previous studies (Tian et al., 2024; Yao et al., 2025; Hussain et al., 2025). For a given prompt \( x \), we conduct multiple samplings using the same model. Subsequently, we calculate the WER for each generation and designate the samples with the lowest and highest WER as \( y^w \) and \( y^f \), respectively. To enlarge the gap between \( y^w \) and \( y^f \), we employ diverse sampling hyperparameters across multiple generations from the same model. Additionally, we use a specific WER threshold to filter out pairs with insufficient performance gaps (more details in Appendix A.3.1).

Inter Pair These pairs are constructed by comparing outputs across different models (Figure 1b). The efficacy of this approach lies in leveraging the complementary strengths of various models. For example, by comparing intra-pairs from different models for the same prompt, we can identify the “best of the best” samples, thereby enhancing the overall quality of positive samples in our dataset. Similar to intra pair, we also employ WER to identify intelligibility preferences for inter pairs (see Appendix A.3.2 for details).

Notably, the proposed inter-pair construction pipeline enables comparative evaluation of intelligibility performance across different models. Using this pipeline, we compared four state-of-the-art models in the field: ARS (Wang et al., 2025a), F5-TTS (Chen et al., 2024c), MaskGCT (Wang et al., 2025a), and CosyVoice 2 (Du et al., 2024b). We constructed 10K inter-pairs and analyzed the win rates of these models, as shown in Table 2. Interestingly, even ARS, the model with the lowest win rate, achieves a 4.1% success rate against the strongest model, CosyVoice 2. This finding validates our assumption regarding the complementary
<table>
  <tr>
    <th></th>
    <th>ARS</th>
    <th>F5-TTS</th>
    <th>MaskGCT</th>
    <th>CosyVoice 2</th>
    <th>Win Rate (↑)</th>
  </tr>
  <tr>
    <th>ARS</th>
    <td>/</td>
    <td>6.7%</td>
    <td>7.4%</td>
    <td>4.1%</td>
    <td>18.3%</td>
  </tr>
  <tr>
    <th>F5-TTS</th>
    <td>10.4%</td>
    <td>/</td>
    <td>8.8%</td>
    <td>5.9%</td>
    <td>25.1%</td>
  </tr>
  <tr>
    <th>MaskGCT</th>
    <td>10.4%</td>
    <td>8.0%</td>
    <td>/</td>
    <td>5.9%</td>
    <td>24.3%</td>
  </tr>
  <tr>
    <th>CosyVoice 2</th>
    <td>11.9%</td>
    <td>10.2%</td>
    <td>10.3%</td>
    <td>/</td>
    <td>32.3%</td>
  </tr>
</table>

* The percentage in each cell represents the proportion of cases where the model on the horizontal axis outperforms the model on the vertical axis.
* The Win Rate is calculated as the sum of values from columns 2 through 5.

Table 2: TTS Intelligibility Arena: We employ the inter-pair construction from INTP to compare intelligibility among four state-of-the-art zero-shot TTS models.

<table>
  <tr>
    <th></th>
    <th>ARS</th>
    <th>F5-TTS</th>
    <th>MaskGCT</th>
    <th>CosyVoice 2</th>
  </tr>
  <tr>
    <th>Positive Samples</th>
    <td>73.0%</td>
    <td>88.1%</td>
    <td>90.9%</td>
    <td>100.0%</td>
  </tr>
  <tr>
    <th>Negative Samples</th>
    <td>45.7%</td>
    <td>15.8%</td>
    <td>47.1%</td>
    <td>75.0%</td>
  </tr>
  <tr>
    <th>All</th>
    <td>59.7%</td>
    <td>53.7%</td>
    <td>64.3%</td>
    <td>90.4%</td>
  </tr>
</table>

Table 3: Human-annotated reading accuracy (↑) for four state-of-the-art zero-shot TTS models on regular texts. We use the intra-pair pipeline of INTP to generate the positive and negative samples.

capabilities among various models.

Perturbed Pair In addition to the aforementioned two types of pairs which are established based on WER, we leverage human knowledge and the intelligence of DeepSeek-V3 (DeepSeek-AI et al., 2024) to create human-guided negative samples, termed perturbed pairs (Figure 1c). The main idea involves deliberately perturbing the input prompt, thereby inducing the model to generate low-quality samples (Majumder et al., 2024; Fu et al., 2024).

Specifically, we design two types of perturbation for the target text in the prompt (as shown in Table 1b): (1) Pronunciation perturbation: we replace certain characters of the text with easily mispronounceable alternatives. For example, given the text “A panda eats shoots and leaves”, we can create the perturbed text “A pan duh eights shots n leafs”. (2) Punctuation perturbation: we modify the punctuation, such as commas, to alter pause patterns and prosody in the text. For example, by adding commas to the text “A panda eats shoots and leaves”, we obtain “A panda eats, shoots, and leaves”, where the words “shoots” and “leaves” transform from nouns in the original text to verbs, creating a significant semantic shift. The detailed process for constructing these perturbed texts is provided in Appendix A.3.3.

3.4 Human Perception Verification

After constructing INTP, we further conducted subjective evaluation to verify its alignment with human perception. For intelligibility alignment, we design a reading accuracy listening task (see Appendix F.3 for details): given a text and a speech, subjects perform binary classification to determine whether the speech accurately reads the text without any content insertion, omission, or mispronunciation. Using four state-of-the-art zero-shot TTS models, we generate 300 intra-pairs on INTP regular texts. The results in Table 3 demonstrate that INTP’s preference identification for intra pairs aligns well with human judgments of intelligibility. Furthermore, comparing Tables 2 and 3 reveals that INTP’s inter-pair comparisons of intelligibility across different models also effectively align with human values.

In addition to intelligibility, we also investigated how well INTP aligns with human preferences for naturalness, which is one of the most general-purpose metrics for TTS (Tan, 2023). The experimental results demonstrate that the naturalness gap between positive and negative samples of INTP is substantial and perceptible to human listeners. We discuss this finding in details in Appendix A.4.

4 Preference Alignment for Diverse Zero-Shot TTS models

In this section, we present methods for achieving preference alignment across a range of TTS models, including autoregressive based, flow-matching based, and masked generative model based architectures. Building on the framework of Direct Preference Optimization (DPO) (Rafailov et al., 2023), initially developed for AR-based models, we adapt and extend its principles to FM-based and MGM-based models. We note that DPO is computationally efficient in practice, and its iterative variant aligns seamlessly with the online reinforcement learning (RL) framework (Li et al., 2024b).

4.1 DPO for AR Models

The main idea of reinforcement learning (RL) for preference alignment is to introduce a reward model \( r(x, y) \) to guide the model for improvement (see e.g., (Li et al., 2024b)). Here \( y \) represents the output (i.e., the generated speech in zero-shot TTS), and \( x \) means the input prompt (i.e., the reference speech and the target text in zero-shot TTS). A widely adopted reward model design is based on Bradley-Terry (BT) model, which defines the probability of preferred sample \( y^w \) over dispreferred sample \( y^l \) given \( x \) as \( p_{\text{BT}}(y^w \succ y^l \mid x) = \)
\( \sigma(r(x, y^w) - r(x, y')) \). We can train the reward model \( r_\phi(x, y) \) by minimizing the negative log-likelihood of observed comparisons from the preference dataset \( \mathcal{D} \):

\[
\mathcal{L}_R = -\mathbb{E}_{(x, y_w, y_l) \sim \mathcal{D}} [\log \sigma (r_\phi(x, y_w) - r_\phi(x, y_l))].
\]

(1)

With the given reward model, the RL optimization objective is to guide the model to maximize the expected reward while minimizing the KL-divergence from a reference distribution:

\[
\max_{p_\theta} \mathbb{E}_{x, y \sim p_\theta(y|x)} [r(x, y)] - \beta D_{KL}[p_\theta(y|x) \parallel p_{ref}(y|x)],
\]

(2)

where the hyperparameter \( \beta \) controls the strength of the regularization. As highlighted in Rafaiov et al. (2023), the optimization problem in Equation 2 admits a closed form solution. This implies a direct relationship between the reward function and the policy. Substituting the reward expression into Equation 1 leads the DPO loss:

\[
\mathcal{L}_{\text{DPO}} = -\mathbb{E}_\mathcal{D} \left[ \log \sigma \left( \beta \left( \log \frac{p_\theta(y_1|x)}{p_{ref}(y_1|x)} - \log \frac{p_\theta(y_0|x)}{p_{ref}(y_0|x)} \right) \right) \right].
\]

(3)

DPO enables direct preference alignment for AR-based TTS models, eliminating the need for explicit reward modeling or RL optimization. In the following subsections, we will introduce its extensions for FM-based and MGM-based TTS models.

4.2 DPO for Flow-Matching Models

The vanilla DPO algorithm is tailored for AR models, while Wallace et al. (2024) extends it to diffusion models. In this subsection, we introduce the DPO algorithm for flow-matching models, specifically demonstrating its application to optimal transport flow-matching (OT-FM), a common approach in FM-based TTS models (Le et al., 2023; Eskimez et al., 2024; Chen et al., 2024c). Given the continuous representation \( y \) of a speech sample and its corresponding condition \( x \), OT-FM constructs a linear interpolation path between Gaussian noise \( y_0 \sim \mathcal{N}(0, I) \) and the target data \( y_1 = y \). Specifically, the interpolation follows \( y_t = (1-t)y_0 + t y_1 \), where \( t \in [0, 1] \), which naturally induces a velocity field \( v_\theta(y_t, t, x) \) that captures the constant directional derivative \( \frac{dy_t}{dt} = y_1 - y_0 \). OT-FM aims to learn the velocity field to match the true derivative. The corresponding loss function is defined as

\[
\mathcal{L}_{\text{OT-FM}} = \mathbb{E}_{y_0, y_1, x, t} \| v_\theta(y_t, t, x) - (y_1 - y_0) \|_2^2,
\]

(4)

where \( t \) is the time step that is sampled from the uniform distribution \( \mathcal{U}(0, 1) \).

Inspired by Wallace et al. (2024), we rewrite the RL objective for flow-matching models. Let \( p_\theta(y_1|y_t, t, x) \) denote our policy that predicts the target sample \( y_1 \) given the noised observation \( y_t \) at time \( t \) and condition \( x \). We initialize from a reference flow-matching policy \( p_{ref} \). The RL objective can be written as:

\[
\begin{aligned}
& \max_{p_\theta} \mathbb{E}_{y_1 \sim p_\theta(y_1|y_t, t, x)} [r(y_1, x)] \\
& \quad - \beta D_{KL}[p_\theta(y_1|y_t, t, x) \| p_{ref}(y_1|y_t, t, x)].
\end{aligned}
\]

(5)

Following a similar derivation process as in DPO (we provide more details in Appendix B.2), we can obtain the loss function for flow-matching DPO:

\[
\begin{aligned}
\mathcal{L}_{\text{DPO-FM}} &= -\mathbb{E}_{(y_1^w, y_1^l, x) \sim \mathcal{D}, t} \\
&\log \sigma \left( \beta \left( \log \frac{p_\theta(y_1^w|y_t^w, t, x)}{p_{ref}(y_1^w|y_t^w, t, x)} - \log \frac{p_\theta(y_1^l|y_t^l, t, x)}{p_{ref}(y_1^l|y_t^l, t, x)} \right) \right),
\end{aligned}
\]

(6)

where \( y_1^w \) and \( y_1^l \) represent the preferred and dis-preferred samples from the preference dataset, respectively, while \( y_t^w \) and \( y_t^l \) are the interpolations at time \( t \) between \( y_1^w \) and \( y_1^l \) and the randomly sampled \( y_0^w \) and \( y_0^l \). The loss can be transformed into the velocity space:

\[
\begin{aligned}
\mathcal{L}_{\text{DPO-FM}} &= -\mathbb{E}_{(y_1^w, y_1^l, x) \sim \mathcal{D}, t} \log \sigma \left( -\beta \right. \\
&\left( \| v_\theta(y_1^w, t, x) - (y_1^w - y_0^w) \|_2^2 - \| v_{ref}(y_1^w, t, x) - (y_1^w - y_0^w) \|_2^2 \right) \\
&- \left( \| v_\theta(y_1^l, t, x) - (y_1^l - y_0^l) \|_2^2 - \| v_{ref}(y_1^l, t, x) - (y_1^l - y_0^l) \|_2^2 \right) \right).
\end{aligned}
\]

(7)

This proposed algorithm can be applied to a wide range of FM-based and diffusion-based TTS models (Le et al., 2023; Eskimez et al., 2024; Shen et al., 2024). In this study, we use it to optimize F5-TTS (Chen et al., 2024c) as a representative.

4.3 DPO for Masked Generative Models

Masked generative model (MGM) is a type of Non-AR generative model, which is also widely adopted in speech generation, as seen in models such as NaturalSpeech 3 (Ju et al., 2024), and MaskGCT (Wang et al., 2025a). MGM aims to recover a discrete sequence \( y = [z_1, z_2, \ldots, z_n] \) from its partially masked version \( y_t = y \odot m_t \), where \( m_t \in \{0, 1\}^n \) is a binary mask sampled via a schedule \( \gamma(t) \in (0, 1] \). MGM is trained to predict masked tokens from unmasked tokens and condition \( x \), modeled as \( p_\theta(y_0 \mid y_t, x) \), optimizing the sum of the marginal cross-entropy for each unmasked token:

\[
\mathcal{L}_{\text{mask}} = -\mathbb{E}_{y, x, t, m_t} \sum_{i=1}^n m_{t,i} \cdot \log p_\theta(z_i \mid y_t, x).
\]

(8)

Using a similar derivation as in Section 4.2, we extend DPO for MGM. Let \( p_{ref}(y_0 \mid y_t, x) \) represent
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="3">Regular cases</th>
    <th colspan="3">Articulatory cases</th>
    <th colspan="3">Code-switching cases</th>
    <th colspan="3">Cross-lingual cases</th>
    <th rowspan="2">Avg</th>
  </tr>
  <tr>
    <th>WER</th><th>SIM</th><th>N-CMOS</th>
    <th>WER</th><th>SIM</th><th>N-CMOS</th>
    <th>WER</th><th>SIM</th><th>N-CMOS</th>
    <th>WER</th><th>SIM</th><th>N-CMOS</th>
  </tr>
  <tr>
    <td>ARS</td>
    <td>3.96</td><td>0.717</td><td>-</td>
    <td>20.03</td><td>0.693</td><td>-</td>
    <td>54.15</td><td>0.693</td><td>-</td>
    <td>19.76</td><td>0.630</td><td>-</td>
    <td>24.47</td><td>0.683</td><td>-</td>
  </tr>
  <tr>
    <td>w/INTP</td>
    <td>2.32</td><td>0.727</td><td>0.47<sup>±0.12</sup></td>
    <td>12.83</td><td>0.713</td><td>0.64<sup>±0.11</sup></td>
    <td>36.91</td><td>0.698</td><td>0.63<sup>±0.01</sup></td>
    <td>9.57</td><td>0.632</td><td>0.82<sup>±0.01</sup></td>
    <td>15.41</td><td>0.692</td><td>0.64<sup>±0.12</sup></td>
  </tr>
  <tr>
    <td>F5-TTS</td>
    <td>3.44</td><td>0.670</td><td>-</td>
    <td>16.84</td><td>0.635</td><td>-</td>
    <td>33.99</td><td>0.609</td><td>-</td>
    <td>16.86</td><td>0.546</td><td>-</td>
    <td>17.78</td><td>0.615</td><td>-</td>
  </tr>
  <tr>
    <td>w/INTP</td>
    <td>2.38</td><td>0.652</td><td>0.38<sup>±0.14</sup></td>
    <td>12.97</td><td>0.628</td><td>0.30<sup>±0.13</sup></td>
    <td>15.98</td><td>0.576</td><td>0.67<sup>±0.01</sup></td>
    <td>7.13</td><td>0.509</td><td>0.47<sup>±0.01</sup></td>
    <td>9.62</td><td>0.591</td><td>0.44<sup>±0.12</sup></td>
  </tr>
  <tr>
    <td>MaskGCT</td>
    <td>2.34</td><td>0.738</td><td>-</td>
    <td>12.43</td><td>0.714</td><td>-</td>
    <td>29.06</td><td>0.696</td><td>-</td>
    <td>12.34</td><td>0.629</td><td>-</td>
    <td>14.04</td><td>0.694</td><td>-</td>
  </tr>
  <tr>
    <td>w/INTP</td>
    <td>2.23</td><td>0.737</td><td>0.23<sup>±0.18</sup></td>
    <td>9.13</td><td>0.722</td><td>0.57<sup>±0.06</sup></td>
    <td>19.70</td><td>0.704</td><td>0.19<sup>±0.18</sup></td>
    <td>7.87</td><td>0.633</td><td>0.29<sup>±0.18</sup></td>
    <td>9.73</td><td>0.699</td><td>0.32<sup>±0.15</sup></td>
  </tr>
  <tr>
    <td>CosyVoice 2</td>
    <td>2.09</td><td>0.709</td><td>-</td>
    <td>8.12</td><td>0.696</td><td>-</td>
    <td>33.36</td><td>0.672</td><td>-</td>
    <td>8.78</td><td>0.600</td><td>-</td>
    <td>13.09</td><td>0.669</td><td>-</td>
  </tr>
  <tr>
    <td>w/INTP</td>
    <td>1.65</td><td>0.709</td><td>0.24<sup>±0.17</sup></td>
    <td>6.87</td><td>0.696</td><td>0.20<sup>±0.16</sup></td>
    <td>28.31</td><td>0.671</td><td>0.63<sup>±0.01</sup></td>
    <td>5.39</td><td>0.603</td><td>0.28<sup>±0.11</sup></td>
    <td>10.56</td><td>0.670</td><td>0.33<sup>±0.12</sup></td>
  </tr>
  <tr>
    <td>Ints</td>
    <td>3.14</td><td>0.688</td><td>-</td>
    <td>12.08</td><td>0.666</td><td>-</td>
    <td>22.88</td><td>0.646</td><td>-</td>
    <td>9.78</td><td>0.572</td><td>-</td>
    <td>11.97</td><td>0.643</td><td>-</td>
  </tr>
  <tr>
    <td>w/INTP</td>
    <td>2.36</td><td>0.686</td><td>0.20<sup>±0.14</sup></td>
    <td>9.38</td><td>0.664</td><td>0.11<sup>±0.12</sup></td>
    <td>13.80</td><td>0.642</td><td>0.20<sup>±0.14</sup></td>
    <td>6.28</td><td>0.571</td><td>0.18<sup>±0.12</sup></td>
    <td>7.96</td><td>0.641</td><td>0.17<sup>±0.15</sup></td>
  </tr>
</table>

Table 4: Improvements of DPO with INTP for different models (AR-based: ARS (Wang et al., 2025a), CosyVoice 2 (Du et al., 2024a), and Ints (Appendix C). FM-based: F5-TTS (Chen et al., 2024c). MGM-based: MaskGCT (Wang et al., 2025a)) on diverse domains. ARS, F5-TTS, and MaskGCT participated in the INTP construction, while CosyVoice 2 and Ints did not.

the reference policy. The DPO loss for MGM is given by:

\[
\mathcal{L}_{\text{DPO-MGM}} = - \mathbb{E}_{(y^w, y^l, x) \sim \mathcal{D}, t} \log \sigma \left( \beta \left( \log \frac{p_\theta(y_0^w | y_t^w, x)}{p_{\text{ref}}(y_0^w | y_t^w, x)} - \log \frac{p_\theta(y_0^l | y_t^l, x)}{p_{\text{ref}}(y_0^l | y_t^l, x)} \right) \right).
\]

Here, \( y_t^w \) and \( y_t^l \) are masked versions of \( y_0^w \) and \( y_0^l \). Note that \( p_\theta(y_0 | y_t, x) \) corresponds to the sum of the log-probabilities of the unmasked tokens in the context of MGM. We provide more details about the derivation in Appendix B.3. In this study, we select MaskGCT (Wang et al., 2025a) as a representative to apply this proposed algorithm for its text-to-codec stage.

5 Experiments

Evaluation Data We evaluate zero-shot TTS systems across diverse domains in both English and Chinese languages. Based on SeedTTS’s evaluation samples (Anastassiou et al., 2024) (which are widely used and also serve as the evaluation set for the pre-trained models of ARS (Wang et al., 2025a), F5-TTS (Chen et al., 2024c), MaskGCT (Wang et al., 2025a), and CosyVoice 2 (Du et al., 2024b) in this study), we construct evaluation sets across four distinct domains: (1) Regular cases: We use SeedTTS test-en (1,000 samples) and SeedTTS test-zh datasets (2,000 samples). (2) Articulatory cases: These involve tongue twisters and repeated texts. For Chinese, we use SeedTTS test-hard, while for English, we use reference speech prompts of SeedTTS test-en, and employ Deepseek-V3 (DeepSeek-AI et al., 2024) to construct the articulatory texts like SeedTTS test-hard. There are 800 samples in total. (3) Code-switching cases: These target texts are a mixture of English and Chinese. Based on SeedTTS test-en and test-zh, we keep their reference speech prompts unchanged, and adopt Deepseek-V3 to transform their texts into code-switching style. There are 1,000 samples in total. (4) Cross-lingual cases: We construct two types of cross-lingual samples: zh2en (500 samples) and en2zh (500 samples). The zh2en means Chinese reference speech (from SeedTTS test-zh) with English target text (from SeedTTS test-en). Similarly for en2zh. The detailed distribution of these sets is presented in Table 11, Appendix F.1.

Evaluation Metrics For objective metrics, we evaluate the intelligibility (WER, ↓), speaker similarity (SIM, ↑), and overall speech quality (UT-MOS (Saeki et al., 2022), ↑). Specifically, for WER, we employ Whisper-large-v3 (Radford et al., 2023) for English, and Paraformer-zh (Gao et al., 2022, 2023) for Chinese and code-switching texts. For SIM, we compute the cosine similarity between the WavLM TDNN (Chen et al., 2022) speaker embeddings of generated samples and the reference speeches. For subjective metrics, we employ Comparative Mean Opinion Score (rated from -2 to 2) to evaluate naturalness (N-CMOS, ↑), use reading accuracy (Section 3.4) to evaluate intelligibility, and use A/B Testing to compare speaker similarity between the generated samples before and after intelligibility alignment. Detailed descriptions of all the metrics are provided in Appendix F.

5.1 Effect of DPO with INTP

To verify the effectiveness of DPO with INTP for existing TTS models, we conduct alignment experiments with multiple models. In addition to ARS, F5-TTS, and MaskGCT, which were used in constructing the INTP dataset, we also introduce two more powerful models in terms of intelligi-
bility: CosyVoice 2 (Du et al., 2024b) and Ints (Appendix C), to validate INTP’s weak-to-strong generalization capability. The experimental results are presented in Table 4, including results on the objective WER, SIM, and the subjective naturalness CMOS.

We observe three key findings from Table 4: (1) Across different evaluation cases, while almost all models demonstrate strong intelligibility performance in regular cases (WER < 4.0), they struggle significantly with articulatory, code-switching, and cross-lingual cases. We show some hallucinated outputs for these domains on our demo website. (2) Comparing across models, CosyVoice 2 and Ints achieves better average intelligibility performance across all cases (WER of 13.09 and 11.97), highlighting the strength of using a textual LLM as the initialization of large-scale TTS model (Du et al., 2024b). (3) Through DPO with INTP, all models, including the more intelligible CosyVoice 2 and Ints that are out of the INTP distribution, show improvements in both intelligibility (WER) and naturalness (N-CMOS), and display comparable performance for speaker similarity (SIM).

Furthermore, we randomly sample 300 samples for subjective evaluation, including assessments of reading accuracy and A/B testing of speaker similarity before and after INTP alignment (see Appendix F.3 for details). The results in Figure 2 demonstrate that INTP alignment enhances all five models in terms of both intelligibility (higher reading accuracy in Figure 2a) and speaker similarity (more Tie/Win percentages in Figure 2b).

5.2 Effect of Different Data within INTP

To investigate the impact of different distributions within INTP, we conduct ablation studies from multiple perspectives. In Table 5, we present three groups of experiments on ARS: the effect of data across different text types, across different models, and the effect of different negative samples. Additional results, including the effect of data across different languages are provided in Appendix E.

We observe three key findings from Table 5: (1) Group 1 demonstrates that different scenarios require customized post-training data. For instance, repeated data proves particularly effective for articulatory cases, while pronunciation-perturbed data significantly improves pronunciation accuracy and WER in cross-lingual cases (see our demo website for details). Moreover, utilizing data from multiple scenarios (i.e., the complete INTP) yields the best overall improvements. (2) Group 2 reveals that model improvement can be achieved through alignment using synthetic data, regardless of whether it’s generated by the model itself or other models. Besides, the intra-pairs and inter-pairs are complementary for model improvements. (3) Group 3 shows that using only positive samples from INTP for supervised fine-tuning (SFT) can already improve quality. Building upon this, incorporating negative samples for preference learning leads to even more substantial gains.

5.3 Iterative Intelligibility Alignment

Furthermore, we explore how to establish an iterative preference alignment, i.e., data and model flywheel (Bai et al., 2022; Dubey et al., 2024; Xiong et al., 2024). This approach aligns with the online reinforcement learning (RL) framework Li et al. (2024b). We investigate two rounds of alignment based on Ints, where Ints v1 (INTP-aligned model) is used to generate new preference data for training Ints v2, following a similar cadence of data collection as (Bai et al., 2022). To prepare Ints v1 generated preference data, we sample a challenging prompt subset from INTP and adopt the same pipeline as INTP to construct preference pairs (see Appendix C.2 for details). The results of this iterative alignment are shown in Table 6. We can observe that compared to Ints v1, Ints v2 yields additional improvements across all scenarios, which demonstrates that effectiveness of iterative alignment. However, we observe that the magnitude of improvement in the second round is notably smaller than the first round. We suspect this indicates that
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="3">Regular cases</th>
    <th colspan="3">Articulatory cases</th>
    <th colspan="3">Code-switching cases</th>
    <th colspan="3">Cross-lingual cases</th>
    <th colspan="3">Avg</th>
  </tr>
  <tr>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
  </tr>
  <tr>
    <td colspan="15"><b>Group 1: Effect of Data across Different Text Types</b></td>
  </tr>
  <tr>
    <td>ARS (Wang et al., 2025a)</td><td>3.96</td><td>0.717</td><td>3.145</td><td>20.03</td><td>0.693</td><td>2.915</td><td>54.15</td><td>0.693</td><td>3.045</td><td>19.76</td><td>0.630</td><td>3.120</td><td>24.47</td><td>0.683</td><td>3.056</td>
  </tr>
  <tr>
    <td>w/ Regular</td><td>2.45</td><td>0.727</td><td>3.200</td><td>17.41</td><td>0.706</td><td>3.000</td><td>37.52</td><td>0.701</td><td>3.110</td><td>9.66</td><td>0.638</td><td>3.200</td><td>16.76</td><td>0.693</td><td>3.135</td>
  </tr>
  <tr>
    <td>w/ Repeated</td><td>2.33</td><td>0.725</td><td>3.225</td><td>12.88</td><td>0.711</td><td>3.050</td><td>39.74</td><td>0.710</td><td>3.150</td><td>10.96</td><td>0.636</td><td>3.235</td><td>16.48</td><td>0.693</td><td>3.165</td>
  </tr>
  <tr>
    <td>w/ Code-switching</td><td>2.32</td><td>0.729</td><td>3.220</td><td>17.67</td><td>0.704</td><td>3.050</td><td>34.20</td><td>0.695</td><td>3.140</td><td>8.69</td><td>0.633</td><td>3.215</td><td>15.72</td><td>0.690</td><td>3.156</td>
  </tr>
  <tr>
    <td>w/ Pronunciation-perturbed</td><td>2.21</td><td>0.720</td><td>3.250</td><td>17.76</td><td>0.693</td><td>3.075</td><td>35.99</td><td>0.687</td><td>3.185</td><td>8.24</td><td>0.617</td><td>3.285</td><td>16.05</td><td>0.679</td><td>3.199</td>
  </tr>
  <tr>
    <td>w/ Punctuation-perturbed</td><td>2.46</td><td>0.722</td><td>3.240</td><td>17.35</td><td>0.699</td><td>3.020</td><td>42.73</td><td>0.694</td><td>3.160</td><td>10.94</td><td>0.624</td><td>3.255</td><td>18.37</td><td>0.684</td><td>3.169</td>
  </tr>
  <tr>
    <td>w/ INTP</td><td>2.32</td><td>0.727</td><td>3.210</td><td>12.83</td><td>0.713</td><td>3.035</td><td>36.91</td><td>0.698</td><td>3.145</td><td>9.57</td><td>0.632</td><td>3.250</td><td>15.41</td><td>0.692</td><td>3.160</td>
  </tr>
  <tr>
    <td colspan="15"><b>Group 2: Effect of Data across Different Models</b></td>
  </tr>
  <tr>
    <td>ARS (Wang et al., 2025a)</td><td>3.96</td><td>0.717</td><td>3.145</td><td>20.03</td><td>0.693</td><td>2.915</td><td>54.15</td><td>0.693</td><td>3.045</td><td>19.76</td><td>0.630</td><td>3.120</td><td>24.47</td><td>0.683</td><td>3.056</td>
  </tr>
  <tr>
    <td>w/ ARS pairs</td><td>2.56</td><td>0.717</td><td>3.200</td><td>13.05</td><td>0.705</td><td>3.015</td><td>40.91</td><td>0.691</td><td>3.125</td><td>11.07</td><td>0.622</td><td>3.225</td><td>16.90</td><td>0.684</td><td>3.141</td>
  </tr>
  <tr>
    <td>w/ MaskGCT pairs</td><td>2.37</td><td>0.724</td><td>3.210</td><td>16.85</td><td>0.700</td><td>3.010</td><td>37.41</td><td>0.692</td><td>3.105</td><td>8.83</td><td>0.625</td><td>3.200</td><td>16.37</td><td>0.685</td><td>3.131</td>
  </tr>
  <tr>
    <td>w/ FS-TTS pairs</td><td>2.46</td><td>0.721</td><td>3.210</td><td>14.99</td><td>0.705</td><td>3.035</td><td>38.77</td><td>0.690</td><td>3.115</td><td>10.01</td><td>0.621</td><td>3.225</td><td>16.56</td><td>0.684</td><td>3.146</td>
  </tr>
  <tr>
    <td>w/ Intra pairs</td><td>2.33</td><td>0.721</td><td>3.200</td><td>15.29</td><td>0.705</td><td>3.015</td><td>37.99</td><td>0.687</td><td>3.115</td><td>9.36</td><td>0.624</td><td>3.200</td><td>16.24</td><td>0.684</td><td>3.133</td>
  </tr>
  <tr>
    <td>w/ Inter pairs</td><td>2.25</td><td>0.726</td><td>3.180</td><td>15.42</td><td>0.703</td><td>2.965</td><td>38.69</td><td>0.697</td><td>3.065</td><td>10.61</td><td>0.631</td><td>3.170</td><td>16.74</td><td>0.689</td><td>3.095</td>
  </tr>
  <tr>
    <td>w/ INTP</td><td>2.32</td><td>0.727</td><td>3.210</td><td>12.83</td><td>0.713</td><td>3.035</td><td>36.91</td><td>0.698</td><td>3.145</td><td>9.57</td><td>0.632</td><td>3.250</td><td>15.41</td><td>0.692</td><td>3.160</td>
  </tr>
  <tr>
    <td colspan="15"><b>Group 3: Effect of Different Negative Samples</b></td>
  </tr>
  <tr>
    <td>ARS (Wang et al., 2025a)</td><td>3.96</td><td>0.717</td><td>3.145</td><td>20.03</td><td>0.693</td><td>2.915</td><td>54.15</td><td>0.693</td><td>3.045</td><td>19.76</td><td>0.630</td><td>3.120</td><td>24.47</td><td>0.683</td><td>3.056</td>
  </tr>
  <tr>
    <td>w/ Regular (SFT)*</td><td>3.28</td><td>0.716</td><td>3.165</td><td>20.03</td><td>0.685</td><td>2.915</td><td>48.73</td><td>0.691</td><td>3.065</td><td>17.25</td><td>0.630</td><td>3.165</td><td>22.32</td><td>0.680</td><td>3.083</td>
  </tr>
  <tr>
    <td>w/ Regular*</td><td>2.45</td><td>0.727</td><td>3.200</td><td>17.41</td><td>0.706</td><td>3.000</td><td>37.52</td><td>0.701</td><td>3.110</td><td>9.66</td><td>0.638</td><td>3.200</td><td>16.76</td><td>0.693</td><td>3.128</td>
  </tr>
  <tr>
    <td>w/ Pronunciation-perturbed*</td><td>2.21</td><td>0.720</td><td>3.250</td><td>17.76</td><td>0.693</td><td>3.075</td><td>35.99</td><td>0.687</td><td>3.185</td><td>8.24</td><td>0.617</td><td>3.285</td><td>16.05</td><td>0.679</td><td>3.199</td>
  </tr>
  <tr>
    <td>w/ Punctuation-perturbed*</td><td>2.46</td><td>0.722</td><td>3.240</td><td>17.35</td><td>0.699</td><td>3.020</td><td>42.73</td><td>0.694</td><td>3.160</td><td>10.94</td><td>0.624</td><td>3.255</td><td>18.37</td><td>0.684</td><td>3.169</td>
  </tr>
  <tr>
    <td colspan="15">* The positive samples in these four experiments are identical. w/ Regular (SFT) refers to supervised fine-tuning using positive samples only, excluding negative samples. w/ Regular employs WER-based negative samples, while the other two utilize our proposed human-guided negative samples.</td>
  </tr>
</table>

Table 5: Effect of different data within INTP for ARS.

<table>
  <tr>
    <th rowspan="2">Model</th>
    <th rowspan="2">Preference Data</th>
    <th colspan="3">Regular cases</th>
    <th colspan="3">Articulatory cases</th>
    <th colspan="3">Code-switching cases</th>
    <th colspan="3">Cross-lingual cases</th>
    <th colspan="3">Avg</th>
  </tr>
  <tr>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
  </tr>
  <tr>
    <td>Ints</td><td></td><td>3.14</td><td>0.688</td><td>3.175</td><td>12.08</td><td>0.666</td><td>3.025</td><td>22.88</td><td>0.646</td><td>3.045</td><td>9.78</td><td>0.572</td><td>3.150</td><td>11.97</td><td>0.643</td><td>3.099</td>
  </tr>
  <tr>
    <td>Ints v1</td><td>INTP</td><td>2.36</td><td>0.686</td><td>3.205</td><td>9.38</td><td>0.664</td><td>3.060</td><td>13.80</td><td>0.642</td><td>3.125</td><td>6.28</td><td>0.571</td><td>3.220</td><td>7.96</td><td>0.641</td><td>3.155</td>
  </tr>
  <tr>
    <td>Ints v2</td><td>INTP</td><td>2.21</td><td>0.686</td><td>3.210</td><td>8.48</td><td>0.660</td><td>3.085</td><td>12.53</td><td>0.643</td><td>3.140</td><td>5.40</td><td>0.567</td><td>3.250</td><td>7.10</td><td>0.639</td><td>3.171</td>
  </tr>
</table>

Table 6: Iterative Preference Alignment for Ints.

the upper bound of iterative alignment is largely determined by the base model’s inherent capabilities, suggesting future research should focus on base models with higher potential.

6 Conclusion

In this work, we focus on the intelligibility issues of modern zero-shot TTS systems across diverse domains, especially in hard-to-pronounce texts, code-switching, and cross-lingual synthesis. We propose to address these challenges using preference alignment with our newly constructed INTP dataset, which contains diverse preference pairs determined through model self-comparison, cross-model comparison, and human guidance. We employ DPO and design special extensions to significantly improve various TTS architectures, while demonstrating INTP’s weak-to-strong generalization capability and establishing an iterative preference alignment flywheel with more powerful base models.

Limitations

While our approach demonstrates significant improvements in zero-shot TTS intelligibility across diverse domains, several limitations remain. Although INTP covers multiple challenging scenarios, it may not fully capture all edge cases, such as specialized jargon or rare language pairs. Future work could expand to more low-resource languages and niche domains. Besides, constructing INTP and conduct alignment experiments on large models like Ints require substantial computational resources, potentially limiting accessibility.

Potential Risks

The proposed method introduces several risks that warrant consideration. Enhanced TTS systems could be exploited to generate deceptive content (e.g., deepfake audio), posing ethical challenges. Robust safeguards and watermarking mechanisms are critical for deployment. While INTP uses public datasets, real-world applications may risk incorporating sensitive or copyrighted speech data, requiring strict governance protocols.

Acknowledgment

This work is partially supported by the NSFC under Grant 62376237, Shenzhen Science and Tech-
nology Program ZDSYS20230626091302006, and Shenzhen Research Institute of Big Data (Internal Project Fund, Grant No. T00120230002). We appreciate Yushun Zhang and the anonymous reviewers for their insightful comments and suggestions.

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An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting Dong, Haoran Wei, Huan Lin, Jialong Tang, Jialin Wang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Ma, Jianxin Yang, Jin Xu, Jingren Zhou, Jinze Bai, Jinzheng He, Junyang Lin, Kai Deng, Keming Lu, Keqin Chen, Kexin Yang, Mei Li, Mingfeng Xue, Na Ni, Pei Zhang, Peng Wang, Ru Peng, Rui Men, Ruize Gao, Runji Lin, Shijie Wang, Shuai Bai, Sinan Tan,
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A Construction Details of INTP

A.1 Prompt Construction

We construct English and Chinese prompt data, both based on the Emilia-Large dataset (He et al., 2024, 2025), which contains diverse real-world speech data across various topics, recording scenarios, and speaking styles.

Reference Speech We perform stratified sampling on Emilia-Large’s speech data based on its metadata such as topics and tags to cover diverse acoustic conditions. Considering the memory constraints of existing zero-shot TTS models during inference, we only select samples with durations not exceeding 12 seconds.

Target Text Similarly to reference speech, we perform stratified sampling based on Emilia-Large’s metadata to cover diverse semantic topics. We select speech samples with durations between 5 and 22 seconds, and use their corresponding textual transcriptions as the target text data source.

We utilize DeepSeek V3 (DeepSeek-AI et al., 2025) to preprocess the sampled textual transcriptions, such as typo correction and punctuation mark normalization, and use the processed text as regular text in INTP. Specifically, we use the following instruction for DeepSeek V3 to conduct text preprocessing:

System Prompt:
I obtained a text from an audio file based on some ASR models. Please help me clean it up (e.g., correct typos, add proper punctuation marks, and make the sentences semantically coherent). Note: (1) You can modify, add, or replace words that better fit the context to ensure semantic coherence. (2) Please only return the cleaned-up result without any explanation.

User Prompt (Example):
a panda eats shoes and leaves

System Output (Example):
A panda eats shoots and leaves.

Furthermore, we employ DeepSeek V3 to transform the regular text into different types. To generate Chinese-English-mixed code-switching texts:

System Prompt:
请你把这句话，转换成一个中文、英文混合的code-switching 版本。注意：你只需要返回给我转换后的结果，不需要任何解释。

User Prompt (Example):
A panda eats shoots and leaves.

System Output (Example):
To generate punctuation-perturbed texts:

System Prompt:
假设你是一个 Text To Speech (TTS) 领域的专家，现在，让我们对一个 TTS 系统进行攻击。具体地：我输入一个文本，请你修改这条文本里面的若干词语，从而使 TTS 系统更容易出错。例如：你可以修改为把某些字修改为容易读错的形式近字、把多音字做替换、等等。但你不要增加和删除原有的文本。注意：你只需要返回给我转换后的结果，不需要任何解释。

例子1:
【我的输入】我今天很高兴
【你的输出】窝锦添狠搞醒

例子2:
【我的输入】目前，爱心人士正在种作寄养的小猫已经五个月大了。而本人的种作寄养申请单需要进一步审核。为了避免小猫多次转手，治疗者们对小猫的种作寄养提出了严格要求：申请人需年满二十三岁。
【你的输出】幕前，爱信人士正在重作寄扬的削猫已经伍个月大了。而本人的重作寄扬神情但需要进一步审核。为了闭面削猫多次转售，治理者们对削猫的重作寄扬提出了阉割要求：申请人需年慢贰拾叁岁。

例子3:
【我的输入】And the idea of standing all by himself in a crowded market, to be pushed and hired by some big, strange farmer, was very disagreeable. Why not sing that high note and grow potatoes?
【你的输出】And the eye dear of standing awl bye himself in a crowd dead market, two bee pushed and high red buy sum big, strange far mer, was vary disagreeable. Y knot sing that hi note and grow poe eight toes?

User Prompt (Example):
A panda eats shoots and leaves.

System Output (Example):
A pan duh eights shots n leafs.

To generate repeated text and punctuation-perturbed text, we leverage DeepSeek V3 to create executable Python scripts that implement rule-based word repetition and random punctuation modification. These scripts will be included in our future open-source repository.

Combination between Speech and Text Based on the language of reference speech and target text data, we design four balanced combination categories: monolingual combinations (en2en and zh2zh) and cross-lingual combinations (zh2en and en2zh), where zh2en denotes Chinese reference speech with English target text, and similarly for others. For each text type shown in Table 1a (Regular, Repeated, Code-Switching, Pronunciation-perturbed, and Punctuation-perturbed), we construct 12K prompts.

A.2 Model Selection

• ARS (Wang et al., 2025a): We use the original checkpoint (pre-trained on Emilia) provided by the authors.

• F5-TTS (Chen et al., 2024c): We use the officially released checkpoint2 for INTP data generation.

• MaskGCT (Wang et al., 2025a): We use the officially released checkpoint3 (Zhang et al., 2024c; Li et al., 2025b) for INTP data generation.

In addition to these three models used for INTP construction, we also investigate INTP’s effectiveness on CosyVoice 2 and Ints. For CosyVoice 2, we conduct alignment experiments using its officially released checkpoint4 as the base model. Details of the pre-trained models of Ints are provided in Appendix C.

A.3 Preference Pairs Construction

A.3.1 Intra Pair
For each model and prompt, we perform five samplings and construct intra pairs based on their WER comparisons. To maximize the performance gap between positive and negative samples, we employ two strategies. First, we use diverse hyperparameters during the five generations to increase sample diversity, selecting the generation with the lowest WER as positive samples and the highest WER as negative samples. Second, we apply a threshold to filter out pairs where the WER gap between positive and negative samples is less than 6.0.

Specifically, for ARS’s five samplings, we set top k to 20 and top p to 1.0, while using different temperatures of 0.4, 0.6, 0.8, 1.0, and 1.2. For F5-TTS and MaskGCT, we use the generated speech target duration as the sampling hyperparameter. Denoting the “ground truth” duration5 as d, we employ five different duration parameters: 0.8d, 0.9d, 1.0d, 1.1d, and 1.2d.

2https://huggingface.co/SWivid/F5-TTS
3https://huggingface.co/amphion/MaskGCT
4https://github.com/FunAudioLM/CosyVoice
5Since we use Emilia-Large’s transcription data as target text in our prompt construction process (Appendix A.1), we refer to the original speech duration corresponding to this transcription as the “ground truth” duration.
<table>
  <tr>
    <th>A+2</th>
    <th>A+1</th>
    <th>Tie</th>
    <th>B+1</th>
    <th>B+2</th>
  </tr>
  <tr>
    <td>10.9%</td>
    <td>29.0%</td>
    <td>15.0%</td>
    <td>32.4%</td>
    <td>12.6%</td>
  </tr>
</table>

* For each pair, we present the two samples to human raters in random order, labeled as A and B. A+2 indicates that sample A’s naturalness is significantly better than B, while A+1 indicates that sample A is moderately better than B, similar for B+2 and B+1. Tie indicates no perceptible difference.

Table 7: Human naturalness preference for 1,000 pairs from INTP regular text domain.

<table>
  <tr>
    <th></th>
    <th>Naturalness Winner</th>
    <th>Naturalness Tie</th>
    <th>Naturalness Loser</th>
  </tr>
  <tr>
    <td>INTP winner</td>
    <td>72%</td>
    <td>15%</td>
    <td>13%</td>
  </tr>
</table>

Table 8: Agreement between INTP preference and human naturalness preference.

A.3.2 Inter Pair

We construct inter pairs based on the intra pairs established in Appendix A.3.1. For a given prompt, we denote model A’s intra pair as \((y_A^w, y_A^l)\) and model B’s intra pair as \((y_B^w, y_B^l)\). We construct inter pairs through three types of comparisons: between \(y_A^w\) and \(y_B^w\), between \(y_A^w\) and \(y_B^l\), and between \(y_A^l\) and \(y_B^w\). Note that we exclude comparisons between \(y_A^l\) and \(y_B^l\) to ensure high quality of positive samples. We apply the same WER threshold as in Appendix A.3.1 to filter out pairs with small performance gaps.

A.3.3 Perturbed Pair

The instructions used to prompt DeepSeek V3 for obtaining pronunciation-perturbed and punctuation-perturbed texts are shown in Appendix A.1. Specifically, we only use data from INTP’s regular text domain to construct perturbed pairs.

A.4 Human Verification

In Section 3.4, we evaluated INTP’s alignment with human intelligibility perception. In this section, we investigate the alignment between INTP and human naturalness preferences. Specifically, we design a naturalness preference annotation task (Appendix F.3). We randomly sample 1,000 pairs from INTP’s regular text domain for human annotation, with results shown in Table 7 and 8. The results reveal two key findings: First, 85% of INTP pairs exhibit distinguishable naturalness preferences (Tie rate of 15% in Table 7). Additionally, INTP’s preference determination shows strong agreement with human naturalness preferences (72% agreement rate between INTP winners and naturalness winners in Table 8). These results suggest that INTP can also serve as a foundation dataset for naturalness preference alignment in future research.

B Details of the Derivation

B.1 DPO for AR Models

Starting from Equation 2, Rafailov et al. (2023) demonstrate that the optimization problem admits a closed-form solution. Specifically, the optimal policy \(p_\theta^*(y|x)\) that maximizes the RL objective is given by:

\[
p_\theta^*(y|x) = \frac{1}{Z(x)} p_{\text{ref}}(y|x) \exp \left( \frac{1}{\beta} r(x, y) \right),
\]

where \(Z(x)\) is the partition function ensuring normalization. This establishes a direct relationship between the reward function and the policy:

\[
r(x, y) = \beta \log \frac{p_\theta^*(y|x)}{p_{\text{ref}}(y|x)} + \beta \log Z(x).
\]

Substituting this reward expression (Equation 11) into the reward modeling loss function (Equation 1) leads the DPO loss (Equation 3), which we represent here as:

\[
\mathcal{L}_{\text{DPO}} = -\mathbb{E}_D \left[ \log \sigma \left( \beta \left( \log \frac{p_\theta(y_{w|t}|x)}{p_{\text{ref}}(y_{w|t}|x)} - \log \frac{p_\theta(y_{l|t}|x)}{p_{\text{ref}}(y_{l|t}|x)} \right) \right) \right].
\]

B.2 DPO for Flow-Matching Models

Starting from Equation 5, which we represent here as:

\[
\max_{p_\theta} \mathbb{E}_{y_1 \sim p_\theta(y_1|x), t, x} [r(y_1, x)]
- \beta \mathbb{D}_{\text{KL}}[p_\theta(y_1|y_t, t, x) \| p_{\text{ref}}(y_1|y_t, t, x)].
\]

Similar to the derivation in DPO (Rafailov et al., 2023) and Wallace et al. (2024), we obtain the closed-form solution for the optimal policy as:

\[
p_\theta^*(y_1|y_t, t, x) = \frac{1}{Z(y_t, t, x)} p_{\text{ref}}(y_1|y_t, t, x) \exp \left( \frac{1}{\beta} r(y_1, x) \right),
\]

where \(Z(y_t, t, x)\) is the partition function ensuring normalization. We can then express the reward model \(r(y_1, x)\) as:

\[
r(y_1, x) = \beta \log \frac{p_\theta^*(y_1|y_t, t, x)}{p_{\text{ref}}(y_1|y_t, t, x)} + \beta \log Z(y_t, t, x).
\]

Similarly, substituting this reward expression (Equation 13) into the reward modeling loss function (Equation 1) leads to the DPO loss for OT-FM (Equation 6), which we represent here:

\[
\mathcal{L}_{\text{DPO-FM}} = -\mathbb{E}_{(y_1^w, y_1^l, x) \sim D, t} \log \sigma \left( \beta \left( \log \frac{p_\theta(y_1^w|y_t^w, t, x)}{p_{\text{ref}}(y_1^w|y_t^w, t, x)} - \log \frac{p_\theta(y_1^l|y_t^l, t, x)}{p_{\text{ref}}(y_1^l|y_t^l, t, x)} \right) \right).
\]
Reviewing the training objective of OT-FM (Equation 4), we find that it is equivalent to fitting a Gaussian likelihood. In other words, the induced likelihood can be interpreted as:

\[
p_\theta(y_1 \mid y_t, t, x) \propto \exp\left(-\frac{1}{\beta} \|v_\theta(y_t, t, x) - (y_1 - y_0)\|_2^2\right),
\]

similarly, for the reference policy, we have:

\[
p_{ref}(y_1 \mid y_t, t, x) \propto \exp\left(-\frac{1}{\beta} \|v_{ref}(y_t, t, x) - (y_1 - y_0)\|_2^2\right).
\]

Here, \( \beta \) serves as an inverse temperature (or noise variance), and the normalization constants cancel out when taking the ratio. By taking the logarithm of the ratio between the learned policy and the reference policy, we obtain:

\[
\log \frac{p_\theta(y_1 \mid y_t, t, x)}{p_{ref}(y_1 \mid y_t, t, x)} = -\frac{1}{\beta} \left( \|v_\theta(y_t, t, x) - (y_1 - y_0)\|_2^2 - \|v_{ref}(y_t, t, x) - (y_1 - y_0)\|_2^2 \right).
\]

Multiplying both sides by \( \beta \) results in:

\[
\beta \log \frac{p_\theta(y_1 \mid y_t, t, x)}{p_{ref}(y_1 \mid y_t, t, x)} = -\left( \|v_\theta(y_t, t, x) - (y_1 - y_0)\|_2^2 - \|v_{ref}(y_t, t, x) - (y_1 - y_0)\|_2^2 \right).
\]

By substituting the log-ratio formulation into Equation 6, we can transform the DPO loss for OT-FM into a form related to the velocity, as shown in Equation 7, which is represented as:

\[
\mathcal{L}_{\text{DPO-FM}} = -\mathbb{E}_{(y_t^w, y_t^l, x) \sim \mathcal{D}, t} \log \sigma \left( -\beta \left( \|v_\theta(y_t^w, t, x) - (y_t^w - y_0^w)\|_2^2 - \|v_{ref}(y_t^w, t, x) - (y_t^w - y_0^w)\|_2^2 \right) - \left( \|v_\theta(y_t^l, t, x) - (y_t^l - y_0^l)\|_2^2 - \|v_{ref}(y_t^l, t, x) - (y_t^l - y_0^l)\|_2^2 \right) \right).
\]

B.3 DPO for Masked Generative Models

Similar to flow-matching, let \( p_\theta(y_0 \mid y_t, x) \) denote the policy to be optimized, and \( p_{ref}(y_0 \mid y_t, x) \) the reference policy. We can rewrite the RL objective for MGM as follows:

\[
\max_{p_\theta} \mathbb{E}_{y_0 \sim p_\theta(y_0|x), t,x} [r(y_0, x)] \\
- \beta \mathrm{D}_{\mathrm{KL}}[p_\theta(y_0|y_t, x) \parallel p_{ref}(y_0|y_t, x)] .
\] (14)

We can also derive the closed-form solution for the optimal policy:

\[
p_\theta^*(y_0|y_t, x) = \frac{1}{Z(y_t, x)} p_{ref}(y_0|y_t, x) \exp \left( \frac{1}{\beta} r(y_0, x) \right),
\] (15)

and express the reward model as follows:

\[
r(y_0, x) = \beta \log \frac{p_\theta^*(y_0|y_t, x)}{p_{ref}(y_0|y_t, x)} + \beta \log Z(y_t, x),
\] (16)

where \( Z(y_t, x) \) is the partition function ensuring normalization. Also, substituting this reward expression (Equation 16) into the reward modeling loss function (Equation 1) leads to the DPO loss for MGM:

\[
\mathcal{L}_{\text{DPO-MGM}} = -\mathbb{E}_{(y_t^w, y_t^l, x) \sim \mathcal{D}, t} \log \sigma \left( \beta \left( \log \frac{p_\theta(y_0^w|y_t^w, x)}{p_{ref}(y_0^w|y_t^w, x)} - \log \frac{p_\theta(y_0^l|y_t^l, x)}{p_{ref}(y_0^l|y_t^l, x)} \right) \right).
\] (17)

Here, \( y_t^w \) and \( y_t^l \) are masked versions of \( y_0^w \) and \( y_0^l \) generated via the mask schedule \( \gamma(t) \). Note that \( p_\theta(y_0|y_t, x) \) corresponds to the sum of the log-probabilities of the unmasked tokens in the context of MGM.

C Ints: Intelligibility-enhanced Speech Language Model

Ints is an intelligibility-enhanced speech language model. It follows a two-stage generation paradigm like (Anastassiou et al., 2024; Du et al., 2024a; Wang et al., 2025a): in the first stage, it uses an AR model to generate discrete speech tokens, while in the second stage, it employs a flow matching model to generate mel-spectrograms from speech tokens. We use the first-layer tokens from DualCodec (Li et al., 2025a) as the modeling target for the first stage of Ints, due to its efficient compression representation (12.5Hz tokens for 24kHz speech) and rich semantic information. Particularly, the first-stage AR model is directly initialized from a large language model while extending the vocabulary to include speech tokens. The codebook size of speech tokens is 16,384. Specifically, in this work, we use the 3.8B Phi-3.5-mini-instruct6 (Abdin et al., 2024), motivated by scaling up model size and leveraging the rich textual semantic knowledge.

C.1 TTS Instruction Design

We format the input as a text-to-speech instruction concatenated with speech tokens. The input sequence is represented as:

\[
[I, T, <|\text{startofspeech}|>, S, <|\text{endofspeech}|>]
\]

where \( I \) is the instruction prefix (e.g., “Please speak the following text out loud”), and \( T \) and \( S \) denote the text and speech token sequences, respectively. The special tokens \( <|\text{startofspeech}|> \) and \( <|\text{endofspeech}|> \) mark the boundaries of the speech token sequence.

6https://huggingface.co/microsoft/Phi-3.5-mini-instruct
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="3">Regular (en)</th>
    <th colspan="3">Articulatory (en)</th>
    <th colspan="3">Code-switching (en2mixed)</th>
    <th colspan="3">Cross-lingual (zh2en)</th>
    <th colspan="3">Avg</th>
  </tr>
  <tr>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
  </tr>
  <tr>
    <td>ARS (Wang et al., 2025a)</td>
    <td>3.55</td><td>0.682</td><td>3.560</td>
    <td>15.98</td><td>0.675</td><td>3.400</td>
    <td>48.59</td><td>0.629</td><td>3.190</td>
    <td>15.22</td><td>0.697</td><td>3.150</td>
    <td>20.84</td><td>0.671</td><td>3.325</td>
  </tr>
  <tr>
    <td>w/ en2en</td>
    <td><b>1.96</b></td><td><b>0.697</b></td><td>3.690</td>
    <td>13.42</td><td>0.685</td><td>3.570</td>
    <td>35.18</td><td>0.641</td><td>3.270</td>
    <td>8.19</td><td>0.692</td><td>3.300</td>
    <td>14.19</td><td>0.679</td><td>3.458</td>
  </tr>
  <tr>
    <td>w/ zh2zh</td>
    <td>2.76</td><td>0.692</td><td>3.660</td>
    <td>13.90</td><td><b>0.687</b></td><td>3.550</td>
    <td>36.65</td><td>0.644</td><td>3.260</td>
    <td>8.92</td><td>0.694</td><td>3.320</td>
    <td>15.06</td><td>0.679</td><td>3.448</td>
  </tr>
  <tr>
    <td>w/ en2zh, zh2en</td>
    <td>2.33</td><td>0.690</td><td><b>3.700</b></td>
    <td><b>11.78</b></td><td>0.684</td><td><b>3.580</b></td>
    <td>35.17</td><td><b>0.645</b></td><td>3.290</td>
    <td><b>7.00</b></td><td>0.700</td><td><b>3.330</b></td>
    <td><b>14.07</b></td><td>0.681</td><td><b>3.475</b></td>
  </tr>
  <tr>
    <td>w/ all</td>
    <td>2.35</td><td>0.695</td><td>3.680</td>
    <td>13.76</td><td>0.686</td><td>3.560</td>
    <td>35.53</td><td>0.642</td><td>3.240</td>
    <td>7.58</td><td>0.704</td><td>3.310</td>
    <td>14.26</td><td>0.682</td><td>3.448</td>
  </tr>
</table>

<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="3">Regular (zh)</th>
    <th colspan="3">Articulatory (zh)</th>
    <th colspan="3">Code-switching (zh2mixed)</th>
    <th colspan="3">Cross-lingual (en2zh)</th>
    <th colspan="3">Avg</th>
  </tr>
  <tr>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
    <th>WER</th><th>SIM</th><th>UTMOS</th>
  </tr>
  <tr>
    <td>ARS (Wang et al., 2025a)</td>
    <td>4.37</td><td>0.752</td><td>2.730</td>
    <td>24.07</td><td>0.711</td><td>2.430</td>
    <td>59.71</td><td>0.756</td><td>2.900</td>
    <td>24.30</td><td>0.563</td><td>3.090</td>
    <td>28.61</td><td>0.696</td><td>2.788</td>
  </tr>
  <tr>
    <td>w/ en2en</td>
    <td>2.68</td><td>0.761</td><td><b>2.760</b></td>
    <td>21.68</td><td>0.727</td><td><b>2.530</b></td>
    <td>48.84</td><td>0.757</td><td>2.990</td>
    <td>12.48</td><td>0.566</td><td>3.140</td>
    <td>21.42</td><td>0.703</td><td><b>2.855</b></td>
  </tr>
  <tr>
    <td>w/ zh2zh</td>
    <td><b>2.41</b></td><td>0.760</td><td>2.740</td>
    <td><b>19.51</b></td><td><b>0.727</b></td><td>2.400</td>
    <td>47.99</td><td>0.755</td><td><b>3.010</b></td>
    <td>12.73</td><td>0.565</td><td>3.110</td>
    <td>20.16</td><td>0.702</td><td>2.838</td>
  </tr>
  <tr>
    <td>w/ en2zh, zh2en</td>
    <td>2.49</td><td><b>0.762</b></td><td>2.740</td>
    <td>22.92</td><td>0.715</td><td>2.490</td>
    <td>41.00</td><td>0.757</td><td>3.000</td>
    <td><b>11.76</b></td><td><b>0.573</b></td><td><b>3.160</b></td>
    <td><b>19.54</b></td><td>0.702</td><td>2.848</td>
  </tr>
  <tr>
    <td>w/ all</td>
    <td>2.62</td><td>0.759</td><td>2.720</td>
    <td>21.06</td><td>0.725</td><td>2.440</td>
    <td>41.50</td><td><b>0.760</b></td><td>2.980</td>
    <td>11.95</td><td>0.572</td><td>3.090</td>
    <td>19.78</td><td><b>0.704</b></td><td>2.808</td>
  </tr>
</table>

Table 9: Effect of different languages within INTP for ARS. In these experiments, we use only the Regular part of INTP for training.

During the inference stage for zero-shot TTS, the input sequence is represented as:

\[
[\mathbf{I}, \mathbf{T}_{\text{prompt}}, \mathbf{T}_{\text{target}}, <|\text{startofspeech}|>, \mathbf{S}_{\text{prompt}}]
\]

to generate the target speech tokens \( S_{\text{target}} \). Here, \( \mathbf{T}_{\text{prompt}} \), \( \mathbf{T}_{\text{target}} \), \( \mathbf{S}_{\text{prompt}} \) are placeholders for the prompt text, target text, and prompt speech tokens, respectively.

C.2 Training data

We pre-train Ints on Emilia (He et al., 2024), which consists of about 100K hours of multilingual data. Following this, we use INTP alignment to obtain Ints v1. Ints v1 is then used to generate new preference data, which are employed to train Ints v2 for iterative alignment. We select prompts from the repeated and code-switching samples of INTP, which can be considered a more challenging subset of prompts. For each prompt, we use the same INTP intra-pair pipeline in Appendix A.3.1 to construct preference pairs.

D Training Details

All of our experiments are conducted on 8 NVIDIA H100 80GB-GPUs. Unless stated otherwise, we use the AdamW optimizer with \( \beta_1 = 0.9, \beta_2 = 0.999 \) and train for one epoch. For each model, we provide more detailed information about the experiments:

• ARS: We use a learning rate of \( 5e-6 \) with a warmup of 4,000 steps and an inverse square root learning scheduler. For DPO, we use the hyperparameter \( \beta = 0.1 \).

• F5-TTS: We use a learning rate of \( 8e-6 \) with a warmup of 4,000 steps and an inverse square root learning scheduler. For DPO, we use the hyperparameter \( \beta = 1,000 \).

• MaskGCT: We use a learning rate of \( 5e-6 \) with a warmup of 4,000 steps and an inverse square root learning scheduler. For DPO, we use the hyperparameter \( \beta = 10 \).

• CosyVoice 2: We use a learning rate of \( 5e-6 \) with a warmup of 4,000 steps and an inverse square root learning scheduler. For DPO, we use the hyperparameter \( \beta = 0.1 \).

• Ints: We use a learning rate of \( 5e-6 \) with a warmup of 4,000 steps and an inverse square root learning scheduler. For DPO, we use the hyperparameter \( \beta = 0.1 \). We use flash attention (Dao et al., 2022) and bfloat16 for training.

E Additional Experimental Results

E.1 Effect of Data across Different Languages within INTP

We present the effect of different languages within INTP in Table 9. The results reveal three key findings: (1) Data from all languages can contribute to improvements across diverse domains for ARS. (2) Interestingly, using only English post-training data (w/ en2en) could also improve performance on Chinese evaluation samples, and vice versa, demonstrating that the proposed alignment algorithm enhances the model’s foundation capability in intelligibility. (3) Furthermore, we again observe the effectiveness of preference alignment’s customized feature: when aiming to improve performance on cross-lingual cases, directly constructing data from
<table>
  <tr>
    <th rowspan="2">Model</th>
    <th colspan="2">Japanese</th>
    <th colspan="2">Korean</th>
    <th colspan="2">German</th>
    <th colspan="2">French</th>
  </tr>
  <tr>
    <th>WER</th><th>SIM</th>
    <th>WER</th><th>SIM</th>
    <th>WER</th><th>SIM</th>
    <th>WER</th><th>SIM</th>
  </tr>
  <tr>
    <th>Ints</th>
    <td>26.34</td><td>0.714</td>
    <td>31.67</td><td>0.708</td>
    <td>28.25</td><td>0.674</td>
    <td>54.53</td><td>0.545</td>
  </tr>
  <tr>
    <th>w/ INTP</th>
    <td>21.82</td><td>0.718</td>
    <td>19.57</td><td>0.741</td>
    <td>21.20</td><td>0.676</td>
    <td>42.12</td><td>0.558</td>
  </tr>
</table>

Table 10: Effect of INTP alignment for unseen languages.

the cross-lingual distribution yields the most significant gains.

E.2 Effect of INTP Alignment for Unseen Languages

We conducted the additional evaluations on four unseen languages not covered by INTP. Specifically, we tested the Ints models before and after INTP alignment using Japanese, Korean, German, and French speech data from GTSinger (Zhang et al., 2024d) (a dataset not used in either pre-training or post-training). We constructed evaluation sets consisting of 500 samples for each language. The results in Table 10 demonstrate that despite INTP containing only Chinese and English data, improvements in both WER and SIM metrics are observed across all four languages. We hypothesize that this generalization stems from our proposed intelligibility preference alignment method enhancing the model’s fundamental capabilities in intelligibility such as the basic articulation and pronunciation.

F Evaluation Details

F.1 Evaluation Data

Our evaluation sets are based on SeedTTS test-en and SeedTTS test-zh datasets7. The SeedTTS test-en set includes 1,000 samples from the Common Voice dataset (Ardila et al., 2019), while the SeedTTS test-zh set comprises 2,000 samples from the DiDiSpeech dataset (Guo et al., 2021). We also provide the detailed distribution of our proposed sets in Table 11.

<table>
  <tr>
    <th rowspan="2">Languages</th>
    <th colspan="2">#Total</th>
  </tr>
  <tr>
    <th>en</th><th>zh</th>
  </tr>
  <tr>
    <td>Regular</td>
    <td>1,000</td><td>2,000</td>
    <td>3,000</td>
  </tr>
  <tr>
    <td>Articulatory</td>
    <td>400</td><td>400</td>
    <td>800</td>
  </tr>
  <tr>
    <td>Code-switching</td>
    <td>en2mixed</td><td>zh2mixed</td>
    <td>1,000</td>
  </tr>
  <tr>
    <td></td>
    <td>500</td><td>500</td>
    <td></td>
  </tr>
  <tr>
    <td>Cross-lingual</td>
    <td>zh2en</td><td>en2zh</td>
    <td>1,000</td>
  </tr>
  <tr>
    <td></td>
    <td>500</td><td>500</td>
    <td></td>
  </tr>
</table>

Table 11: Statistics of the proposed evaluation sets in four scenarios (en: English, zh: Chinese, mixed: mixture of English and Chinese, zh2en: Chinese reference speech with English target text. Similarly for en2mixed, zh2mixed, and en2zh).

Paraformer–zh9 (Gao et al., 2022, 2023) for Chinese and code-switching texts.

• SIM: We compute the cosine similarity between the WavLM TDNN10 (Chen et al., 2022) speaker embeddings of generated samples and the prompt samples.

• UTMOS: We use the pretrained UTMOS strong learner following the official implementation11.

F.3 Subjective Evaluation

We consider four different settings: regular, articulatory, code-switching, and cross-lingual. Each setting is evaluated in two languages, resulting in 10 samples per language. This setup yields a total of 80 pairs. These 80 pairs are evaluated across 5 different systems (ARS, F5-TTS, MaskGCT, CosyVoice 2, and Ints), leading to a total of 400 pairs. We engage 20 participants in the evaluation process, ensuring that each sample is assessed at least three times.

We conduct subjective evaluations from three perspectives: intelligibility (reading accuracy), naturalness (N-CMOS), and speaker similarity (A/B

7https://github.com/BytedanceSpeech/seed-tts-eval
8https://huggingface.co/openai/whisper-large-v3
9https://huggingface.co/funasr/paraformer-zh
10https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification
11https://github.com/sarulab-speech/UTMOS22
Figure 3: User interface for intelligibility and naturalness evaluation.

Figure 4: User interface for speaker similarity evaluation.

Testing). We have developed an automated subjective evaluation interface, as shown in Figure 3 and Figure 4. For each item to be evaluated, users will see three components: the System Interface, the Questionnaire, and the Evaluation Criteria.

Intelligibility (Reading Accuracy):

• System Interface: Users listen to the speech audio and compare it to the provided target text to assess whether the speech matches the text.

• Questionnaire: Users are asked, “Is any reading error? (insertion, omission, or mispronunciation)”

• Evaluation Criteria: The evaluation is binary: “No Error” (the speech matches the text) or “Has Error” (the speech does not match the text).

Naturalness (N-CMOS):

• System Interface: Users listen to two speech samples, A and B, to compare their naturalness.

• Questionnaire: Users are asked, “Which speech sounds more natural?”

• Evaluation Criteria: Options include A +2 (Sample A is much more natural), A +1 (Sample A is slightly more natural), Tie (Both are equally natural), B +1 (Sample B is slightly more natural), and B +2 (Sample B is much more natural).

Speaker Similarity (A/B Testing):

• System Interface: Users listen to two speech samples, A and B, to evaluate their similarity to the speech of the reference speaker.

• Questionnaire: Users are asked, “Which speech sounds more like the reference speaker’s style?”

• Evaluation Criteria: Options include A +2 (Sample A is much more similar), A +1 (Sample A is slightly more similar), Tie (Both are equally similar), B +1 (Sample B is slightly more similar), and B +2 (Sample B is much more similar).

Metareview score: 4
Metareview: This paper improves zero-shot TTS intelligibility using a new dataset (INTP) and preference alignment methods (DPO and extensions to various architectures). It targets scenarios like tongue twisters, repeated words, code-switching, and cross-lingual synthesis.

Review summary:

* All reviewers appreciate the practical contribution — a large-scale preference dataset and comprehensive experiments across TTS architectures, validating improvements in intelligibility with both objective and subjective metrics.
 * The paper is praised for the breadth of testing, inclusion of human evaluations, and potential open-sourcing. 
* However, several reviewers note that the technical novelty is moderate. Some point out that DPO extensions are standard and that improvements primarily stem from dataset construction and experimental thoroughness rather than algorithmic innovation. Additionally, a few reviewers mention issues like reliance on WER for dataset construction (even though human validation mitigates it), minor SIM degradation for one model (F5-TTS), and a request for clearer separation between the dataset and method sections to improve readability. 
* One reviewer raised initial concerns about generalization, but after rebuttal experiments on unseen languages and tasks, acknowledged the method’s broader applicability.

Meta reviewer post-all (including confidential comments/non-acknowledged responses) summary: While the core method generalizes standard techniques (preference learning with DPO), the paper shows strong empirical contributions through a large, practical dataset and systematic evaluation.  While for a long paper, pure novelty could be higher, the empirical and practical contributions justify acceptance.
Summary of reasons to publish: The paper shows strong empirical contributions through a large, practical dataset and systematic evaluation and covers diverse domain tasks.


Please treat the score as a 4 if the paper is submitted to the *ACL dataset topic. 3.5+  as a speech paper under the *ACL ML/modeling topic.