Knowledge-Enhanced Large Language Models for Joint Lyrics Understanding and Singing Voice Generation
Keywords:
large language models; singing voice generation; lyrics understanding; knowledge-enhanced architectures; generative audio; data governance; music information retrievalAbstract
Large language models have established new capabilities in conditional text generation, but singing voice generation requires simultaneous control over semantic content, prosody, pitch, rhythm, voice identity, and emotional expression. This paper examines knowledge-enhanced large language model architectures for joint lyrics understanding and singing voice generation from a systems perspective. The discussion analyzes how structured musical knowledge, multilingual lyric representations, retrieval augmentation, and domain-specific tokenization shape the trade-offs among intelligibility, expressiveness, and computational cost. Architectural alternatives including autoregressive token prediction, latent diffusion over acoustic representations, and hybrid encoder-decoder designs are compared with attention to scalability, latency, and controllability. The paper further examines deployment infrastructure constraints, licensing and provenance of singing corpora, robustness to noisy or adversarial lyrics, fairness across languages and vocal identities, and sustainability implications of large-scale training and inference. By synthesizing insights from language modeling, speech synthesis, music information retrieval, and socio-technical governance, the paper argues that joint lyrics understanding and singing voice generation should be treated as a coupled sociotechnical system rather than a purely acoustic optimization problem. Evaluation paradigms, data documentation, and policy mechanisms must evolve alongside model architectures to support culturally diverse, accountable, and sustainable music generation.
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