Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.12958

Emo-BPO: Emotion Bidirectional Preference Optimization for Diffusion-based Emotional TTS

Jiacheng Shi·Hongfei Du·Xinyuan Song·Y. Alicia Hong·Yanfu Zhang·Ye Gao
View original source ↗Read source document ↗Export metadata ↓

Abstract

Diffusion-based TTS has demonstrated strong capability in emotional speech generation. However, existing emotion alignment methods primarily reinforce target emotional expressions while overlooking modeling of competing emotional modes, limiting fine-grained controllability. This issue becomes more pronounced under classifier-free guidance (CFG), which relies on contrasting conditional trajectories to shape generation. We propose Emotion Bidirectional Preference Optimization (Emo-BPO), a simple effective framework that jointly learns emotion-aligned and emotion-contrastive score functions from reordered same-text emotional pairs. Our method requires neither auxiliary reward models nor additional annotations, and can be seamlessly integrated into existing diffusion-based TTS systems to improve emotional controllability and expressiveness.

Explore related subjects

cs.SDeess.AS

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jiacheng Shi, Hongfei Du, Xinyuan Song, Y. Alicia Hong, Yanfu Zhang, Ye Gao. 2026-10-09. Emo-BPO: Emotion Bidirectional Preference Optimization for Diffusion-based Emotional TTS. https://arxiv.org/abs/2610.12958

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

KEEP EXPLORING

Related papers

arXiv2026

Bayesian Speech Synthesizers Can Learn from Multiple Teachers

Text-to-speech (TTS) is a "one-to-many" problem: one text can be spoken in many valid ways, so its realizations form a distribution whose spread depends on the input. Most TTS models do not model this spread explicitly: they leave it implicit in the sampling process or fix it in advance. To model this distribution explicitly, we propose BELLE (Bayesian evidential learning with language modelling), which places a Bayesian prior over the mean and variance of each mel-spectrogram frame and predicts this prior from the input, while keeping the architecture and single-pass sampling of common continuous-valued autoregressive TTS models such as MELLE. Estimating this variance requires several recordings of each text, whereas standard TTS corpora provide one. We therefore construct two parallel-speech corpora: ParallelVox from real human recordings and LS+6TTS, which adds speech from six pre-trained TTS teachers to LibriSpeech (LS). Trained on these corpora, BELLE outperforms MELLE under the same training conditions, is competitive with larger TTS systems on the objective metrics of LibriSpeech test-clean, and supports streaming generation. Analyses of repeated human readings show that real speech has the properties the Bayesian prior assumes and that the variance predicted by BELLE follows the variation across these readings. Parallel speech improves BELLE, and synthetic parallel speech performs comparably to real parallel speech at the same volume and is far easier to scale.

cs.SD↗
arXiv2026

AdaptDuplex: From Static to Adaptive Full-Duplex Spoken Dialogue

Full-duplex spoken dialogue requires simultaneous listening and speaking at sub-second latency, under conversational timing and cognitive demands that change moment to moment. Yet current models mostly impose static operating points, lacking a systematic mechanism for adaptive decisions. We present AdaptDuplex, which extends Qwen3-Omni with such a mechanism, co-designed across three layers. A compact token-level protocol represents every window as a canonical sequence, trains dual-stream alignment through a bounded text lead over speech, and exposes every behavioral decision as an explicit token for training-free runtime control via logits bias. Adaptive mechanisms dynamically predict among discrete window durations and augment direct response as needed with non-blocking cognitive consolidation and multi-flight external reasoning. A progressive pipeline introduces these behaviors through a three-stage Thinker curriculum, then Talker-only and joint SFT, with GRPO as a preliminary increment. On Full-Duplex-Bench v1 and v1.5, AdaptDuplex outperforms DuplexOmni on 18 of 21 comparable turn-taking, overlap-behavior, and timing metrics and MiniCPM-o 4.5 on 19 of 22, with gains in both interaction decisions and response timing. On human-recorded HumDial-FDBench, it attains the top Final score (69.6) of the compared duplex models.

cs.SD↗
arXiv2026

LiveSynth: A Streaming Neural Synthesizer for Instrument Cloning and Text-to-Instrument

Recently, generative neural synthesizers have enabled zero-shot instrument cloning from reference recordings and timbre generation from text prompts. However, existing systems generate complete audio segments offline, precluding their use as playable instruments. We present LiveSynth, a streaming neural synthesizer with flexible timbre control and interactive generation capabilities, including zero-shot instrument cloning and text-to-instrument synthesis. A feedback-free causal Transformer conditioned on MIDI events and a CLAP timbre embedding outputs continuous audio latents at 100 Hz, which a causal variational autoencoder decodes into audio. Frame-wise timbre conditioning enables continuous timbre morphing, while MIDI-conditioning dropout enables autonomous continuation from a performed prefix. On the NSynth and Slakh datasets, LiveSynth achieved timbre similarity and spectral reconstruction quality comparable to offline baselines. On an M3 MacBook Air, LiveSynth required 8.8 ms of computation per 10 ms audio frame (an RTF of 0.88) with 10 ms algorithmic latency, enabling real-time interaction with a performer.

cs.SD↗