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Found 15 papers, 3 papers with code
QualiSpeech: A Speech Quality Assessment Dataset with Natural Language Reasoning and Descriptions
To bridge this gap, we introduce QualiSpeech, a comprehensive low-level speech quality assessment dataset encompassing 11 key aspects and detailed natural language comments that include reasoning and contextual insights.
World Modeling Makes a Better Planner: Dual Preference Optimization for Embodied Task Planning
Recent advances in large vision-language models (LVLMs) have shown promise for embodied task planning, yet they struggle with fundamental challenges like dependency constraints and efficiency.
Audio Large Language Models Can Be Descriptive Speech Quality Evaluators
Recent advances have enabled large language models (LLMs) to incorporate auditory systems for handling various speech-related tasks.
SALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation
Unlike traditional modularised conversational AI systems, which separate speech recognition, understanding, and text-to-speech generation into distinct components, multimodal LLMs operate as single end-to-end models.
Enabling Auditory Large Language Models for Automatic Speech Quality Evaluation
The results demonstrate that auditory LLMs achieve competitive performance compared to state-of-the-art task-specific small models in predicting MOS and SIM, while also delivering promising results in A/B testing and natural language descriptions.
Robust Zero-Shot Text-to-Speech Synthesis with Reverse Inference Optimization
By leveraging reverse inference as the standard to select exemplars used in RLHF from the speech samples generated by the TTS system itself, RIO steers the subsequent optimization towards a direction of enhancing the TTS robustness.
Cross-Modality Safety Alignment
As Artificial General Intelligence (AGI) becomes increasingly integrated into various facets of human life, ensuring the safety and ethical alignment of such systems is paramount.
Bayesian Example Selection Improves In-Context Learning for Speech, Text, and Visual Modalities
Large language models (LLMs) can adapt to new tasks through in-context learning (ICL) based on a few examples presented in dialogue history without any model parameter update.
In-Memory Learning: A Declarative Learning Framework for Large Language Models
The exploration of whether agents can align with their environment without relying on human-labeled data presents an intriguing research topic.
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis
Domain adaption has been widely adapted for cross-domain sentiment analysis to transfer knowledge from the source domain to the target domain.
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation
HAG extends the current paradigm in the text generation process, highlighting the feasibility of endowing the LLMs with self-regulate decoding strategies.
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis
Few-shot text classification has attracted great interest in both academia and industry due to the lack of labeled data in many fields.
Evaluating Hallucinations in Chinese Large Language Models
We analyze the primary types of hallucinations in different types of models and their causes.
Can Whisper perform speech-based in-context learning?
Language-level adaptation experiments using Chinese dialects showed that when applying SICL to isolated word ASR, consistent and considerable relative WER reductions can be achieved using Whisper models of any size on two dialects, which is on average 32. 3%.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Causal Intervention Improves Implicit Sentiment Analysis
In this work, we propose a causal intervention model for Implicit Sentiment Analysis using Instrumental Variable (ISAIV).
