While audio large language models have achieved remarkable progress in auditory perception, they still lag behind text-based large language models in deep logical reasoning capabilities. This performance disparity primarily stems from the scarcity of high-quality audio reasoning data. To bridge this modality gap, we propose a novel cross-modal on-policy distillation framework. Our approach leverages a powerful text model as a teacher to provide dynamic correction and guidance to the audio foundational model during on-policy exploration. Furthermore, we construct a three-tier symmetric dataset encompassing logical reasoning, complex audio reasoning, and speech dialogue. By dynamically aligning the student model’s reasoning trajectories within the high-noise auditory space with the teacher model’s prior knowledge in the clean text space, our method effectively distills robust reasoning capabilities into the audio domain. Extensive experiments demonstrate that our model achieves substantial improvements on the audio and speech understanding benchmarks, paving a new path for comprehensive audio-language understanding.

While diffusion and flow-matching models have advanced TTS, generating high-arousal emotions remains a persistent challenge due to the trade-off between stability and expressiveness. Existing systems often suffer from linguistic collapse when pursuing high intensity or fail to meet target emotional levels under stable settings. In this work, we identify that standard Gaussian initialization inevitably introduces a neutral prosody bias, while uniform Classifier-Free Guidance often distorts the acoustic manifold, leading to artifacts. To address this, we propose an inference framework that rectifies the emotional trajectory. An Emotion-Rectified Noise Prior injects a semantic gradient at initialization to align sampling with the target emotional manifold, and Likelihood-Inverse Guidance adaptively schedules guidance via a conditional/unconditional likelihood ratio, strengthening guidance only when the trajectory drifts toward a neutral fallback. Extensive experiments demonstrate that our method effectively resolves the stability bottleneck in high-intensity scenarios, achieving superior linguistic accuracy and emotional fidelity without model retraining. Audio samples are available at https://showtts.github.io/emotionTTS/.

Extensive research on LLM-based spoken dialogue systems has significantly advanced the development of intelligent voice assistants. However, the integration of role information within speech remains an underexplored area, limiting its application in real-world scenarios, particularly in multi-party dialogue settings. With the growing demand for personalization, voice assistants that can recognize and remember users establish a deeper connection with them. We focus on enabling LLMs with speaker-awareness capabilities and enhancing their understanding of character settings through synthetic data to generate contextually appropriate responses. We introduce Persona-Dialogue, the first large-scale multi-party spoken dialogue dataset that incorporates speaker profiles. Based on this dataset, we propose PAChat, an architecture that simultaneously models both linguistic content and speaker features, allowing LLMs to map character settings to speaker identities in speech. Through extensive experiments, we demonstrate that PAChat successfully achieves speaker-specific responses, character understanding, and the generation of targeted replies in multi-party dialogue scenarios, surpassing existing spoken dialogue systems.