A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement
In this study, we present an approach to train a single speech enhancement network that can perform both personalized and non-personalized speech enhancement. This is achieved by incorporating a frame-wise conditioning input that specifies the type of enhancement output. To improve the quality of the enhanced output and mitigate oversuppression, we experiment with re-weighting frames by the presence or absence of speech activity and applying augmentations to speaker embeddings. By training under a multi-task learning setting, we empirically show that the proposed unified model obtains promising results on both personalized and non-personalized speech enhancement benchmarks and reaches similar performance to models that are trained specialized for either task. The strong performance of the proposed method demonstrates that the unified model is a more economical alternative compared to keeping separate task-specific models during inference.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-Task LearningSpeech EnhancementSimilar Papers 제목 키워드 기반
Unified Personalized Understanding, Generating and Editing
Unified large multimodal models (LMMs) have achieved remarkable progress in general-purpose multimodal understanding and generation. However, they still operate under a ``one-size-fits-all'' paradigm and struggle to mode…
Image EditingTest-Time Personalization: A Diagnostic Framework and Probabilistic Fix for Scaling Failures
Existing approaches to LLM personalization focus on constructing better personalized models or inputs, while treating inference as a single-shot process. In this work, we study Test-Time Personalization (TTP) along an un…
Text GenerationPP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity
Personalized news recommendation methods are widely used in online news services. These methods usually recommend news based on the matching between news content and user interest inferred from historical behaviors. Howe…
DiversityNews RecommendationFrom Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning
Agentic reinforcement learning (Agentic RL) has achieved strong progress in tasks with clear success signals. However, many real-world agent applications require user-conditioned behavior: the same query may call for dif…
Reinforcement LearningDesign Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models
Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models driven by click-through-rate (CTR) to controllably create attractive image …
Text Generation