Search arXiv⌕ Search

arXiv · 2610.29123

Character-Level Prosody Control for Mandarin Speech Synthesis:An Architecture-Preserving Approach

Abstract

This paper addresses fine-grained prosody control for large-language-model (LLM) based text-to-speech (TTS) synthesis of Mandarin Chinese, motivated by multimedia content-creation scenarios---film dubbing, audiobook production, and interactive voice interfaces---in which prosodic adjustments must be placed on specific characters rather than applied uniformly to whole utterances. We present a data-driven framework in which fine-grained prosodic markers (pitch, duration, energy, and pause) are automatically extracted from raw audio at scale and embedded into the text stream as out-of-vocabulary Unicode symbols. The LLM of an existing TTS system is then fine-tuned to interpret these markers while its architecture remains unchanged, yielding character-level temporal control that complements existing utterance-level style control. An optional text-LLM front-end can translate natural-language instructions into marker sequences. Experiments on 2.15M Mandarin utterances (5,858 hours) with CosyVoice2 as the backbone demonstrate reliable control of character duration, pitch, pause duration, and global speaking rate, with perceptual quality essentially preserved. A cross-tokenizer analysis further indicates that the ASR-optimized tokenizer of the backbone attenuates prosodic variation relative to reconstruction-oriented alternatives---an observation we report as hypothesis-generating evidence for prosody-sensitive system design.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Diankun An, Lu Ma, Yongliang Wang, Zhewen Su, Xin Tian. 2026-10-21. Character-Level Prosody Control for Mandarin Speech Synthesis:An Architecture-Preserving Approach. https://arxiv.org/abs/2610.29123

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Combining Masked Language Modeling and Cross-Modal Contrastive Learning for Prosody-Aware TTS

Text encoders in neural text-to-speech (TTS) see only linguistic content, so prosody must be inferred from paired data and synthesised intonation is often flat. Contrastive text-audio pretraining (CLAPSpeech) injects prosodic knowledge by contrasting acoustic realisations of the same phoneme, but it never trains the encoder to tell different phonemes apart, and its interaction with masked language modelling (MLM) is unexplored. We study a curriculum for a speaker-conditioned phoneme-and-subword encoder: MLM, contrastive alignment over batches of different phonemes, and CLAPSpeech-style same-phoneme refinement. On Russian conversational speech, with each variant conditioning an identical Grad-TTS model, MLM followed by mixed-phoneme alignment gives the lowest word error rate (17.6% versus 32.8% for same-phoneme training alone) and the best quality and intonation ratings. Adding same-phoneme refinement improves same-phoneme retrieval yet yields the worst synthesis: embedding-space retrieval does not predict usefulness for generation.

cs.SD↗

Beyond Naturalness: Probing Automated Text-To-Speech Evaluators on Linguistically Grounded Dimensions

Automated Text-to-Speech (TTS) evaluation methods (Mean Opinion Score (MOS) predictors and Audio Large Language Model (Audio-LLM) judges) are expected to reflect human perception, yet it is unclear how well they capture the distinct aspects of speech that listeners actually perceive. We deconstruct "naturalness" into a linguistically grounded annotation schema spanning 10 distinct perceptual dimensions, and use it to construct the first dimension-level meta-evaluation benchmark for TTS, comprising 860 utterances annotated by trained linguist raters. Results from benchmarking four MOS predictors and four Audio-LLM judges reveal that MOS predictors are more sensitive to acoustic signal-level properties at the prosodic and paralinguistic levels, while Audio-LLM judges show selective, prompt-dependent detection that favors word-level errors and does not generalize across all dimensions. Neither class reliably captures a breadth of linguistically structured, human-perceptible speech errors. Our dataset, annotation schema, and evaluation code are publicly released to support more targeted and interpretable TTS evaluation.

cs.SD↗

Qwen-Audio-Jev: A Unified Interface for Audio Decisions

Large audio language models (LALMs) have made substantial progress in audio understanding. However, autoregressive text generation introduces computational overhead and latency in high-throughput applications that require frequent selection, judgment, and scoring. To address this limitation, we introduce Qwen-Audio-Jev, which augments Qwen-Audio-3.1-ASR-Flash-Next with a dedicated Jev decision head. The model unifies selection, binary judgment, and scoring through three decision primitives: CHOICE, NOUL, and SCORE. Given an audio input, a natural-language question, and an explicit answer specification, Qwen-Audio-Jev directly outputs a task-conditioned probability distribution or a numerical score through typed decision readouts. Evaluation across 18 benchmarks demonstrates the model's effectiveness: CHOICE achieves 78.40 percent accuracy on MMAU and 83.14 percent on MMSU; NOUL achieves 93.76 percent accuracy on the VoxCeleb1-O speaker verification benchmark; and SCORE achieves a normalized mean absolute error of 0.1235 on NISQA overall quality assessment. These results highlight the potential of Qwen-Audio-Jev for efficient audio decision-making in applications such as large-scale audio screening, speaker verification, and automated quality assessment.

cs.SD↗