Challenge: Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing.
Approach: They propose a singing-voice-synthesis method that enables attribute controlling on singer gender, vocal range and volume with natural language.
Outcome: The proposed method achieves favorable control ability and audio quality.

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TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis (2025.findings-acl)

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Challenge: Existing zero-shot singing voice synthesis models depend on phoneme and note boundary annotations, limiting their robustness and producing poor transitions between phonemes and notes.
Approach: They propose a multi-task multilingual zero-shot SVS model with style transfer and style control based on various prompts.
Outcome: Experimental results show that TCSinger 2 outperforms baseline models in subjective and objective metrics across multiple related tasks.
Self-Supervised Singing Voice Pre-Training towards Speech-to-Singing Conversion (2024.findings-acl)

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Challenge: Existing studies on speech-to-singing voice conversion (STS) are limited by the scarcity of paired speech-song data and the suboptimal quality of outputs.
Approach: They propose a self-supervised singing voice pre-training model that transforms a speech-to-singing voice into a paired singing voice.
Outcome: The proposed model improves both STS and singing voice synthesis tasks by combining spoken language and a self-supervised singing voice pre-training model.
STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation (2025.findings-acl)

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Challenge: Existing automated singing annotation (ASA) methods tackle isolated aspects of the annotation pipeline.
Approach: They propose a framework that addresses transcription, alignment, and refined style annotations.
Outcome: The proposed framework delivers comprehensive multi-level annotations encompassing: (1) precise phoneme-audio alignment, (2) robust note transcription and temporal localization, (3) expressive vocal technique identification, and (4) global stylistic characterization including emotion and pace.
TCSinger: Zero-Shot Singing Voice Synthesis with Style Transfer and Multi-Level Style Control (2024.emnlp-main)

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Challenge: Existing models fail to generate singing voices rich in stylistic nuances for unseen singers due to multifaceted nature of singing styles.
Approach: They propose a zero-shot SVS model for style transfer across cross-lingual speech and singing styles and multi-level style control.
Outcome: Experimental results show that TCSinger outperforms baseline models in synthesis quality, singer similarity, and style controllability.
Versatile Framework for Song Generation with Prompt-based Control (2025.findings-emnlp)

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Challenge: Existing methods for song generation fail to generate vocals with prompt-based control and proper alignment.
Approach: VersBand is a multi-task song generation framework for synthesizing high-quality songs with prompt-based control.
Outcome: Experimental results show that VersBand performs better than baseline models across multiple song generation tasks.
RMSSinger: Realistic-Music-Score based Singing Voice Synthesis (2023.findings-acl)

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Challenge: Existing methods for singing voice synthesis are limited to fine-grained music scores . manual adjustment destroys regularity of note durations, making fine-grain music scores "crushed"
Approach: They propose a method to synthesize singing voices given realistic music scores . they use real-music-score-based Singing Voice Synthesis to generate high-quality voices .
Outcome: The proposed method eliminates manual annotation and simplifies phoneme-level mel-note alignment.
Towards Controllable Speech Synthesis in the Era of Large Language Models: A Systematic Survey (2025.emnlp-main)

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Challenge: Text-to-speech (TTS) has advanced from generating natural-sounding speech to enabling fine-grained control over speech attributes.
Approach: They provide a review of controllable TTS methods from traditional control techniques to emerging approaches using natural language prompts.
Outcome: The proposed methods are based on models, strategies, and features, and summarize challenges, datasets, and evaluations.
Robust Singing Voice Transcription Serves Synthesis (2024.acl-long)

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Challenge: Current AST methods struggle with accuracy and robustness when used for practical annotation.
Approach: They propose a model that converts singing recordings into note sequences for automatic annotation of singing datasets.
Outcome: The proposed model outperforms baseline models on enlarged, automatically annotated datasets.
Text-to-Song: Towards Controllable Music Generation Incorporating Vocal and Accompaniment (2024.acl-long)

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Challenge: Existing studies focus on singing voice synthesis and music generation independently.
Approach: They propose a novel task called Text-to-Song synthesis which incorporates both vocal and accompaniment generation.
Outcome: The proposed method can synthesize songs with comparable quality and style consistency.
Improving Chinese Pop Song and Hokkien Gezi Opera Singing Voice Synthesis by Enhancing Local Modeling (2023.emnlp-main)

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Challenge: Singing Voice Synthesis (SVS) synthesizes pleasing vocals based on music scores and lyrics . current acoustic models ignore the significance of local modeling within the sequence and the hard-to-synthesize parts in the predicted mel-spectrogram .
Approach: They propose a method to enhance local modeling in the acoustic model by focusing on phoneme tokens located before and after the phoneme.
Outcome: The proposed method improves local modeling in the acoustic model by focusing on the hard-to-synthesize parts of the predicted mel-spectrogram.

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