While recent Spoken Language Models (SLMs) have been actively deployed in real-world scenarios, they lack the capability to discern Third-Party Interruptions (TPI) from the primary user's ongoing flow, leaving them vulnerable to contextual failures.To bridge this gap, we introduce TPI-Train, a dataset of 88K instances designed with speaker-aware hard negatives to enforce acoustic cue prioritization for interruption handling, and TPI-Bench, a comprehensive evaluation framework designed to rigorously measure the interruption-handling strategy and precise speaker discrimination in deceptive contexts.Experiments demonstrate that our dataset design mitigates semantic shortcut learning-a critical pitfall where models exploit semantic context while neglecting acoustic signals essential for discerning speaker changes.We believe our work establishes a foundational resource for overcoming text-dominated unimodal reliance in SLMs, paving the way for more robust multi-party spoken interaction.The code for the framework is publicly available at https://tpi-va.github.io/.↩→ * **Definition:** A Task Enhancer helps the VA fulfill the user's request to VA more accurately, or efficiently.* **Examples of NonIgnorable interruptions include, but are not limited to:** * **Corrections or Disambiguations:** This might help the VA resolve an ambiguity or fix an error in the user's query.* *(e.g., User: "Call my brother," Third Party: "You mean your older brother, Mark, right?")* * **Cooperative Additions or Refinements:** This could give the VA extra specifics to better fulfill or understand the request.↩→ * *(e.g., User: "Add coffee to the shopping list," Third Party: "Get the decaf one.")** **Feasibility Constraints:** This could alert the VA to real-world conditions that may prevent or affect the request.* *(e.g., User: "Let's play music in the garden," Third Party: "The portable speaker's battery is dead.")** **Goal-oriented Suggestions:** This could give the VA an alternative that better achieves the user's intended outcome.* *(e.g., User: "How do I get to the airport?"Third Party: "The subway will be much faster than a taxi at this hour.")***2.Ignorable:** The interruption is irrelevant to complete and understand the user's ongoing request better.The VA should disregard it as it does not contribute to fulfilling the request.↩→ * **Definition:** The information is off-topic, a side comment, or directed at another human without impacting the VA's task.↩→ * **Example of an Ignorable interruption:** * *(e.g., User: "Set a timer for 10 minutes," Third Party: "I wonder what's for dinner tonight.")*### Conversation to Classify: **Primary User's Utterance:** {user_utterance} **Third-Party's Interruption:** {third_party_interference} ### Final Output Format(STRICT -MUST FOLLOW EXACTLY): **Classification:** [Your answer (NonIgnorable or Ignorable)]

We propose EdiText, a controllable text editing method that modifies the reference text to desired attributes at various scales. We integrate an SDEdit-based editing technique that allows for broad adjustments in the degree of text editing. Additionally, we introduce a novel fine-level editing method based on self-conditioning, which allows subtle control of reference text. While being capable of editing on its own, this fine-grained method, integrated with the SDEdit approach, enables EdiText to make precise adjustments within the desired range. EdiText demonstrates its controllability to robustly adjust reference text at a broad range of levels across various tasks, including toxicity control and sentiment control.

We present NanoVoice, a personalized text-to-speech model that efficiently constructs voice adapters for multiple speakers simultaneously. NanoVoice introduces a batch-wise speaker adaptation technique capable of fine-tuning multiple references in parallel, significantly reducing training time. Beyond building separate adapters for each speaker, we also propose a parameter sharing technique that reduces the number of parameters used for speaker adaptation. By incorporating a novel trainable scale matrix, NanoVoice mitigates potential performance degradation during parameter sharing. NanoVoice achieves performance comparable to the baselines, while training 4 times faster and using 45 percent fewer parameters for speaker adaptation with 40 reference voices. Extensive ablation studies and analysis further validate the efficiency of our model.

When applying parameter-efficient finetuning via LoRA onto speaker adaptive text-to-speech models, adaptation performance may decline compared to full-finetuned counterparts, especially for out-of-domain speakers. Here, we propose VoiceGuider, a parameter-efficient speaker adaptive text-to-speech system reinforced with autoguidance to enhance the speaker adaptation performance, reducing the gap against full-finetuned models. We carefully explore various ways of strengthening autoguidance, ultimately finding the optimal strategy. VoiceGuider as a result shows robust adaptation performance especially on extreme out-of-domain speech data. We provide audible samples in our demo page.