Recently, Automatic Speech Recognition (ASR) systems (e.g., Whisper) have achieved remarkable accuracy improvements but remain highly sensitive to real-world unseen data (data with large distribution shifts), including noisy environments and diverse accents. To address this issue, test-time adaptation (TTA) has shown great potential in improving the model adaptability at inference time without ground-truth labels, and existing TTA methods often rely on pseudo-labeling or entropy minimization. However, by treating model confidence as a learning signal, these methods may reinforce high-confidence errors, leading to confirmation bias that undermines adaptation. To overcome these limitations, we present ASR-TRA, a novel Test-time Reinforcement Adaptation framework inspired by causal intervention. More precisely, our method introduces a learnable decoder prompt and utilizes temperature-controlled stochastic decoding to generate diverse transcription candidates. These are scored by a reward model that measures audio-text semantic alignment, and the resulting feedback is used to update both model and prompt parameters via reinforcement learning. Comprehensive experiments on LibriSpeech with synthetic noise and L2 Arctic accented English datasets demonstrate that our method significantly outperforms existing state-of-the-art (SOTA), including SUTA and SGEM, in both accuracy and inference speed. Ablation studies further confirm the effectiveness of combining audio and language-based rewards, highlighting our method's enhanced stability and interpretability. Overall, our approach provides a practical and robust solution for deploying ASR systems in challenging real-world conditions.

Text-to-speech (TTS) has advanced from generating natural-sounding speech to enabling fine-grained control over attributes like emotion, timbre, and style.Driven by rising industrial demand and breakthroughs in deep learning, e.g., diffusion and large language models (LLMs), controllable TTS has become a rapidly growing research area.This survey provides the first comprehensive review of controllable TTS methods, from traditional control techniques to emerging approaches using natural language prompts.We categorize model architectures, control strategies, and feature representations, while also summarizing challenges, datasets, and evaluations in controllable TTS.This survey aims to guide researchers and practitioners by offering a clear taxonomy and highlighting future directions in this fast-evolving field.One can visit https://github.com/imxtx/ awesome-controllabe-speech-synthesis for a comprehensive paper list and updates.

Cued Speech (CS) is a visual coding system that combines lip movements and hand gestures to represent spoken languages for hearing-impaired people. Automatic Cued Speech Recognition (ACSR) is an emerging research topic, but the cuer (i.e., people who perform CS) generalization problem of ACSR remains unexplored. Moreover, the asynchrony between lip and hand modalities further aggravates the challenges associated with the generalization problem. Therefore, we propose a novel multi-modal, cuer-invariant representation learning framework that facilitates generalizable ACSR through contrastive learning, enabling our model to recognize unseen cuers. The proposed approach comprises three key components: 1) an inter-sentence contrastive learning module that learns cuer-invariant representations for lip and hand modalities to solve the cuer generalization problem in ACSR; and 2) a multi-level intra-sentence cross-attention module that synchronizes the lip movements and hand gestures to address the modality asynchrony issue in ACSR; 3) an easy-to-hard progressive learning strategy to stabilize the learning process and prevent performance degradation on hard examples. Extensive experiments on two available CS datasets show that our method outperforms previous works by a large margin. We also conduct ablation studies and visualization to demonstrate the effectiveness of the proposed method.

Facial recognition (FR) technology offers convenience in our daily lives, but it also raises serious privacy issues due to unauthorized FR applications. To protect facial privacy, existing methods have proposed adversarial face examples that can fool FR systems. However, most of these methods work only in the digital domain and do not consider natural physical protections. In this paper, we present NatMask, a 3D-based method for creating natural and realistic adversarial face masks that can preserve facial identity in the physical world. Our method utilizes 3D face reconstruction and differentiable rendering to generate 2D face images with natural-looking facial masks. Moreover, we propose an identity-aware style injection (IASI) method to improve the naturalness and transferability of the mask texture. We evaluate our method on two face datasets to verify its effectiveness in protecting face identity against four state-of-the-art (SOTA) FR models and three commercial FR APIs in both digital and physical domains under black-box impersonation and dodging strategies. Experiments show that our method can generate adversarial masks with superior naturalness and physical realizability to safeguard face identity, outperforming SOTA methods by a large margin.