While controllable Text-to-Speech (TTS) has achieved notable progress, most existing methods remain limited to inter-utterance-level control, making fine-grained intra-utterance expression challenging due to their reliance on non-public datasets or complex multi-stage training. In this paper, we propose TED-TTS, a training-free controllable framework for pretrained zero-shot TTS to enable intra-utterance emotion and duration expression. Specifically, we propose a segment-aware emotion conditioning strategy that combines causal masking with monotonic stream alignment filtering to isolate emotion conditioning and schedule mask transitions, enabling smooth intra-utterance emotion shifts while preserving global semantic coherence. Based on this, we further propose a segment-aware duration steering strategy to combine local duration embedding steering with global EOS logit modulation, allowing local duration adjustment while ensuring globally consistent termination. To eliminate the need for segment-level manual prompt engineering, we construct a 30,000-sample multi-emotion and duration-annotated text dataset to enable LLM-based automatic prompt construction. Extensive experiments demonstrate that our training-free method not only achieves state-of-the-art intra-utterance consistency in multi-emotion and duration control, but also maintains baseline-level speech quality of the underlying TTS model. Audio samples are available at https://aclanonymous111.github.io/TED-TTS-DemoPage/.

Current singing voice synthesis systems often struggle in multi-singer scenarios due to limited training data that only includes a few singers. Existing zero-shot multi-singer singing voice synthesis systems are criticized for their reliance on global timbre embeddings from single reference audio, which fail to capture sufficient timbre details. This paper introduces SPSinger, a multi-singer singing voice synthesizer that generates singer-specific voices from brief reference audio (around 5 seconds) without prior training on the singer’s voice. SPSinger builds on the StableDiffusion framework by adding a global encoder to capture consistent timbre features from short reference prompts and an attention-based local encoder to capture detailed variations from long prompts, used only during training. To overcome the challenge of requiring long audio prompts during inference, we introduce the Latent Prompt Adaptation Model (LPAM), a Transformer-based module that derives timbre features from global embeddings. This approach eliminates the need for long reference prompts. Additionally, we propose a novel pitch shift algorithm that uses LPAM to predict the pitch shift values. Our experiments show that SPSinger achieves high-quality singing voice synthesis that preserves the identity of the target singer, even when using only short reference audio inputs in zero-shot scenarios.

The precise control of singing techniques is of utmost importance in achieving emotionally expressive vocal performances. To bridge the gap between current Singing Voice Synthesis (SVS) systems and human singers, our paper focuses on developing an SVS system that allows for control over singing techniques. In this paper, we introduce SinTechSVS, a singing technique controllable SVS system composed of a singing technique annotator, a singing technique controllable synthesizer, and a singing technique recommender. Our approach leverages transfer learning for efficient singing technique annotation and adapts the DiffSinger framework with additional style encoders and an attention-based singing technique local score (STLS) module to enhance singing technique controllability. We also propose a Seq2Seq singing technique recommender for the new task of Singing Technique Recommendation (STR). Experimental results demonstrate that SinTechSVS significantly improves the quality and expressiveness of synthesized vocal performances, with comparable general synthesis capabilities to state-of-the-art SVS systems and enhanced control over singing techniques, as evidenced by objective and subjective evaluations. To the best of our knowledge, SinTechSVS is the first SVS capable of controlling singing techniques.