Papers by Jialong Zuo

8 papers
VoxpopuliTTS: a large-scale multilingual TTS corpus for zero-shot speech generation (2025.coling-main)

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Challenge: Existing multilingual TTS datasets are limited in speech generation fields due to lack of quality data.
Approach: They propose to use 30,000 hours of high-quality speech data across 3 languages . they filter out low-quality text-text pairs and concatenate short transcripts .
Outcome: The proposed dataset comprises 30,000 hours of high-quality speech data, across 3 languages with multiple speakers and styles, suitable for various speech tasks such as TTS and ASR.
CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-long)

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Challenge: Cross-modal retrieval tasks are used to retrieve data from one modality or another based on a query from another modality.
Approach: They propose a generative cross-modal retrieval framework based on coarse-to-fine semantic modeling . they propose combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation.
Outcome: The proposed framework achieves excellent performance and efficiency in multimodal retrieval tasks.
Language-Codec: Bridging Discrete Codec Representations and Speech Language Models (2025.acl-long)

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Challenge: Existing gaps between discrete acoustic codecs and downstream speech language models . initial channel of codebooks contains excessive information, making it difficult to generate tokens from weakly supervised signals such as text.
Approach: They propose a discrete acoustic codec for generating acustic tokens from weakly supervised signals.
Outcome: The proposed language-codec outperforms competing audio compression algorithms and validates on downstream speech language models.
AudioVSR: Enhancing Video Speech Recognition with Audio Data (2024.emnlp-main)

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Challenge: Recent work has shown poor performance with non-Indo-European languages . previous work primarily utilizes video information to build VSR models .
Approach: They propose a generative model for data inflation that integrates synthetic data with authentic visual data to enhance the VSR model.
Outcome: The proposed model improves on the audio-visual alignment problem in audio-video tasks.
Rhythm Controllable and Efficient Zero-Shot Voice Conversion via Shortcut Flow Matching (2025.acl-long)

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Challenge: Existing methods focus on disentangling speakers and content, while others focus on preserving the source's prosody.
Approach: They propose a rhythm-controllable and efficient zero-shot voice conversion model that transforms the source speaker’s timbre into an unseen one while retaining speech content.
Outcome: The proposed model adapts the linguistic content duration to the desired speaking style, facilitating the transfer of the target speaker’s rhythm.
FluentSpeech: Stutter-Oriented Automatic Speech Editing with Context-Aware Diffusion Models (2023.findings-acl)

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Challenge: Existing methods for speech editing still suffer from over-smoothing problem and lack of robustness due to stutter.
Approach: They propose a stutter-oriented automatic speech editing model that incorporates sutter information into the hidden sequence.
Outcome: The proposed model achieves state-of-the-art performance on a speech recording dataset . it can improve fluency of stuttering speech in terms of objective and subjective metrics.
ControlSpeech: Towards Simultaneous and Independent Zero-shot Speaker Cloning and Zero-shot Language Style Control (2025.acl-long)

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Challenge: Prior zero-shot TTS models only mimic the speaker’s voice without further control and adjustment capabilities while prior controllable TTS systems cannot perform speaker-specific voice generation.
Approach: They propose a style control module that captures codec representations corresponding to timbre, content, and style in a discrete decoupling codec space.
Outcome: The proposed system can fully clone the speaker's voice and perform speech-specific adjustment and control functions.
MobileSpeech: A Fast and High-Fidelity Framework for Mobile Zero-Shot Text-to-Speech (2024.acl-long)

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Challenge: Existing zero-shot text-to-speech systems require a few seconds of unseen speaker voice prompts to generate high-quality voices.
Approach: They propose a zero-shot text-to-speech system based on mobile devices . they use a discrete speech codec to integrate hierarchical information from the codec .
Outcome: The proposed system achieves RTF of 0.09 on a single A100 GPU and has been successfully deployed on mobile devices.

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