Chunyu Qiang, Xiaopeng Wang, Kang Yin, Yuzhe Liang, Yuxin Guo, Teng Ma, Ziyu Zhang, Tianrui Wang, Cheng Gong, Yushen Chen, Ruibo Fu, Longbiao Wang, Jianwu Dang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Speech editing has garnered more and more attention due to its diverse applications. However, existing systems often require substantial manual effort or have limited capabilities in attribute editing, imposing significant constraints. In this work, we present SpeechPalette, a comprehensive high-quality speech editing method that allows users to easily modify various attributes of the selected speech segment according to their preferences. Specifically, the proposed model approaches speech editing from a decoupling perspective, disentangling critical information such as text, pitch, duration and more from the input speech. Then, reconstruction is achieved through a mask and prediction mechanism. Furthermore, we leverage a diffusion model to predict the residuals between the real and predicted speech, further enhancing synthesis quality. The proposed method not only excels at text-based speech editing but also handles tasks involving pitch and speed rate adjustments. Moreover, it also demonstrates remarkable performance in one-shot text-to-speech scenarios. While recent large-scale models achieve impressive synthesis quality through massive computational resources, SpeechPalette offers a balanced approach with explicit fine-grained control over speech attributes, practical deployment requirements, and competitive performance relative to similarly-sized systems. Experimental results across a range of tasks consistently demonstrate the superior performance of our method compared to baseline systems. Additionally, comprehensive ablation studies validate the effectiveness of our proposed approach.

Mainstream Text-to-Audio (TTA) models that rely on Mel-spectrograms often struggle to generate audio with rich content, leading to blurred or incoherent outputs. This stems from an inability to model intricate spectral details and textures. We investigate the role of U-Net components in generation and find that high-frequency components in skip-connections and the backbone are crucial for texture, while low-frequency backbone components are vital for the denoising process. Based on this, we propose “Mel-Refine,” a plug-and-play approach that enhances Mel-spectrogram quality by adjusting component weights during inference. Our method requires no additional training or fine-tuning and is fully compatible with any diffusion-based TTA architecture. Experiments show that Mel-Refine boosts the performance of the latest TTA model, Tango2, by $25 \%$, demonstrating its effectiveness.

In this paper, we present the system developed by our team, CCATTS, for the LIMMITS’25 challenge, focusing on few-shot and zero-shot TTS. We adopt a two-stage TTS strategy. In track 1, we fine-tune the pre-trained ZMM-TTS model and successfully achieve multilingual low-latency TTS. In track 2, we propose a token-based framework by modifying the first-stage model of ZMM-TTS, disentangling speech features into four types of discrete tokens—Content, Acoustic, Emotion, and Speaker—and integrating it with our designed token2wav module. This module consists of a HiFi-GAN-style decoder, an acoustic refiner, and U-Net flow matching, to generate high-quality speech. The official competition results demonstrate that our method achieves strong performance in both tracks.

In recent years, speech diffusion models have advanced rapidly. Alongside the widely used U-Net architecture, transformer-based models such as the Diffusion Transformer (DiT) have also gained attention. However, current DiT speech models treat Mel spectrograms as general images, which overlooks the specific acoustic properties of speech. To address these limitations, we propose a method called Directional Patch Interaction for Text-to-Speech (DPI-TTS), which builds on DiT and achieves fast training without compromising accuracy. Notably, DPI-TTS employs a low-to-high frequency, frame-by-frame progressive inference approach that aligns more closely with acoustic properties, enhancing the naturalness of the generated speech. Additionally, we introduce a fine-grained style temporal modeling method that further improves speaker style similarity. Experimental results demonstrate that our method increases the training speed by nearly 2 times and significantly outperforms the baseline models.

Expressive voice conversion (EVC) aims to modify the speaker identity and emotional style of speech while preserving its content. Existing approaches often focus on disentangling speaker, emotion, and content information but overlook the progressive generation mechanisms in human speech production. To address this, we propose a three-stage framework that includes a speech disentanglement module, a progressive generator, and an acoustic refiner. This framework enables speech pre-trained models to parse linguistic content, emotional style, and speaker identity, which are then progressively integrated into the speech reconstruction branch to generate high-quality speech with replaceable emotional style and speaker identity. Experiments with six different pre-trained models show that our framework activates their disentanglement capabilities, surpassing baseline performance in EVC, and supports speaker and emotion control from different target samples. This framework also provides a valuable reference for evaluating the disentanglement capabilities of speech pre-training models.

While emotional text-to-speech (TTS) has made significant progress, most existing research remains limited to utterance-level emotional expression and fails to support word-level control. Achieving word-level expressive control poses fundamental challenges, primarily due to the complexity of modeling multi-emotion transitions and the scarcity of annotated datasets that capture intra-sentence emotional and prosodic variation. In this paper, we propose WeSCon, the first self-training framework that enables word-level control of both emotion and speaking rate in a pretrained zero-shot TTS model, without relying on datasets containing intra-sentence emotion or speed transitions. Our method introduces a transition-smoothing strategy and a dynamic speed control mechanism to guide the pretrained TTS model in performing word-level expressive synthesis through a multi-round inference process. To further simplify the inference, we incorporate a dynamic emotional attention bias mechanism and fine-tune the model via self-training, thereby activating its ability for word-level expressive control in an end-to-end manner. Experimental results show that WeSCon effectively overcomes data scarcity, achieving state-of-the-art performance in word-level emotional expression control while preserving the strong zero-shot synthesis capabilities of the original TTS model.

Recent advancements in zero-shot text-to-speech have enabled the generated speech to preserve both the speaker's identity and emotion, guided by a reference speech. However, generating speech in emotional styles that the target speaker has never exhibited remains a central challenge in style transfer. Existing methods attempt to achieve this through disentangled modeling of speaker and style representations, yet insufficient disentanglement often leads to weak expressiveness and speaker leakage. To address these limitations, we propose a robust LM-based TTS framework that enables fine-grained control over speaker identity and emotional style. Specifically, we introduce a frame-level Style Transfer Pitch Predictor to capture fine-grained prosodic and speaker-related information, and a Speaker Emotional Style Alignment module that strengthens emotional expressiveness while preserving speaker identity. Our method enables precise control over the intensity of emotional style in the synthesized speech. Experimental results demonstrate that our approach outperforms existing methods, delivering more expressive and controllable emotional synthesized speech in the emotional style transfer task. Audio samples can be available athttps://whyrrrrun.github.io/ICST.github.io/.

Recently, there has been a growing interest in text-to-speech (TTS) methods that can be trained with minimal supervision by combining two types of discrete speech representations and using two sequence-to-sequence tasks to decouple TTS. However, existing methods suffer from three problems: the high-frequency waveform distortion of discrete speech representations, the prosodic averaging problem caused by the duration prediction model in non-autoregressive frameworks, and difficulty in prediction due to the information redundancy and dimension explosion of existing semantic coding methods. To address these problems, three progressive methods are proposed. First, we propose Diff-LM-Speech, an autoregressive structure consisting of a language model and diffusion models, which models the semantic embedding into the mel-spectrogram based on a diffusion model to achieve higher audio quality. We also introduce a prompt encoder structure based on a variational autoencoder and a prosody bottleneck to improve prompt representation ability. Second, we propose Tetra-Diff-Speech, a non-autoregressive structure consisting of four diffusion model-based modules that design a duration diffusion model to achieve diverse prosodic expressions. Finally, we propose Tri-Diff-Speech, a non-autoregressive structure consisting of three diffusion model-based modules that verify the non-necessity of existing semantic coding models and achieve the best results. Experimental results show that our proposed methods outperform baseline methods. We provide a website with audio samples.1

For fine-grained generation and recognition tasks such as minimally-supervised text-to-speech (TTS), voice conversion (VC), and automatic speech recognition (ASR), the intermediate representations extracted from speech should serve as a "bridge" between text and acoustic information, containing information from both modalities. The semantic content is emphasized, while the paralinguistic information such as speaker identity and acoustic details should be de-emphasized. However, existing methods for extracting fine-grained intermediate representations from speech suffer from issues of excessive redundancy and dimension explosion. Contrastive learning is a good method for modeling intermediate representations from two modalities. However, existing contrastive learning methods in the audio field focus on extracting global descriptive information for downstream audio classification tasks, making them unsuitable for TTS, VC, and ASR tasks. To address these issues, we propose a method named "Contrastive Token-Acoustic Pretraining (CTAP)", which uses two encoders to bring phoneme and speech into a joint multimodal space, learning how to connect phoneme and speech at the frame level. The CTAP model is trained on 210k speech and phoneme pairs, achieving minimally-supervised TTS, VC, and ASR. The proposed CTAP method offers a promising solution for fine-grained generation and recognition downstream tasks in speech processing. We provide a website with audio samples.1

Text-to-speech (TTS) methods have shown promising results in voice cloning, but they require a large number of labeled text-speech pairs. Minimally-supervised speech synthesis decouples TTS by combining two types of discrete speech representations(semantic & acoustic) and using two sequence-to-sequence tasks to enable training with minimal supervision. However, existing methods suffer from information redundancy and dimension explosion in semantic representation, and high-frequency waveform distortion in discrete acoustic representation. Autoregressive frameworks exhibit typical instability and uncontrollability issues. And non-autoregressive frameworks suffer from prosodic averaging caused by duration prediction models. To address these issues, we propose a minimally-supervised high-fidelity speech synthesis method, where all modules are constructed based on the diffusion models. The non-autoregressive framework enhances controllability, and the duration diffusion model enables diversified prosodic expression. Contrastive Token-Acoustic Pretraining (CTAP) is used as an intermediate semantic representation to solve the problems of information redundancy and dimension explosion in existing semantic coding methods. Mel-spectrogram is used as the acoustic representation. Both semantic and acoustic representations are predicted by continuous variable regression tasks to solve the problem of high-frequency fine-grained waveform distortion. Experimental results show that our proposed method outperforms the baseline method. We provide audio samples on our website.1

Recent progress has propelled the development of realistic talking-face videos for avatars. Yet, animating 3D cartoon avatars remains intricate due to the imprecise nature of facial-driven data. This often manifests as inconsistent mouth configurations and rigid facial expressions, curbing the animation’s realism. Addressing these issues, we introduce a conformer-based framework that derives expression coefficients directly from phonemes, thereby elevating prediction precision and minimizing manual oversight. Furthermore, by harnessing a pre-trained emotion blending module coupled with the keyframe of the target emotional character, we employ a zero-shot adaptation technique. This serves to amplify emotional expressions and bolster the authenticity of lip dynamics. Our methodology adeptly registers nuanced expression shifts in avatars, leading to remarkably lifelike animations, as substantiated by our experimental findings.

Self-supervised learning (SSL) representations from massively multilingual models offer a promising solution for low-resource language speech tasks. Despite advancements, language adaptation in TTS systems remains an open problem. This paper explores the language adaptation capability of ZMM-TTS, a recent SSL-based multilingual TTS system proposed in our previous work. We conducted experiments on 12 languages using limited data with various fine-tuning configurations. We demonstrate that the similarity in phonetics between the pretraining and target languages, as well as the language category, affects the target language’s adaptation performance. Additionally, we find that the fine-tuning dataset size and number of speakers influence adaptability. Surprisingly, we also observed that using paired data for fine-tuning is not always optimal compared to audio-only data. Beyond speech intelligibility, our analysis covers speaker similarity, language identification, and predicted MOS.

Cross-speaker style transfer in speech synthesis aims at transferring a style from source speaker to synthesized speech of a target speaker’s timbre. In most previous methods, the synthesized fine-grained prosody features often represent the source speaker’s average style, similar to the one-to-many problem(i.e., multiple prosody variations correspond to the same text). In response to this problem, a strength-controlled semi-supervised style extractor is proposed to disentangle the style from content and timbre, improving the representation and interpretability of the global style embedding, which can alleviate the one-to-many mapping and data imbalance problems in prosody prediction. A hierarchical prosody predictor is proposed to improve prosody modeling. We find that better style transfer can be achieved by using the source speaker’s prosody features that are easily predicted. Additionally, a speaker-transfer-wise cycle consistency loss is proposed to assist the model in learning unseen style-timbre combinations during the training phase. Experimental results show that the method outperforms the baseline. We provide a website with audio samples1.

End-to-end framework can generate high-quality and high-similarity speech in the personalized speech synthesis task. However, the generalization of out-of-domain texts is still a challenging task. Limited target data leads to unacceptable errors and poor prosody and similarity performance of the synthetic speech. In this paper, we present a bi-level function decoupling framework to realise separate modeling and controlling for solving above problems. Firstly, on the style representation modeling level, compared with the conventional methods that use single embedding to model all the text dependent discrepancies, it is proposed that the speaker embedding and prosody embedding are modeled separately based on the reference audio and phonetic posteriorgram (PPG) by a multi-head attention mechanism. Secondly, on the model structure level, the decoder model structure is factored into average-net and adaptation-net, where the duration prosody controlling and speaker timbre imitation are mainly designed in relatively separate areas. Experimental results on Mandarin dataset show that the proposed methods lead to an improvement on both robustness, naturalness and similarity.

The paper describes the CASIA speech synthesis system entry for challenge M2VoC 2021. The low similarity and naturalness of synthesized speech remains a challenging problem for speaker adaptation with few resources. Since the end-to-end acoustic model is too complex to interpret, overfitting will occur when training with few data. To prevent the model from overfitting, this paper proposes a novel speaker adaptation framework that decomposes the prosody and voice characteristics in the end-to-end model. A prosody control attention is proposed to control the phonemes’ duration of different speakers. To make the attention controlled by the prosody information, a set of phoneme-level transition tokens is auto-learned from the prosody encoder in our framework and these transition tokens can determine the duration of phonemes in the attention mechanism. Secondly, when we need to use small data set for speaker adaptation, we just need to adapt the speaker related prosody model and decoder, which can prevent the model from overfitting. Further, we use a data puring model to automatically optimize the quality of datasets. Experiments demonstrate the effectiveness of speaker adaptation based on our method, and we (team identifier is T03) get the top three results in competition M2VoC by using this framework.