Search Results for author:
Found 27 papers, 10 papers with code
AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems
Introducing AgiBot World, a large-scale platform comprising over 1 million trajectories across 217 tasks in five deployment scenarios, we achieve an order-of-magnitude increase in data scale compared to existing datasets.
AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement
As AI models are increasingly deployed across diverse real-world scenarios, ensuring their safety remains a critical yet underexplored challenge.
Uni-Retrieval: A Multi-Style Retrieval Framework for STEM's Education
In this paper, we propose a diverse expression retrieval task tailored to educational scenarios, supporting retrieval based on multiple query styles and expressions.
MinMo: A Multimodal Large Language Model for Seamless Voice Interaction
Previous models for voice interactions are categorized as native and aligned.
OmniFlatten: An End-to-end GPT Model for Seamless Voice Conversation
However, achieving low latency and natural interactions in full-duplex dialogue systems remains a significant challenge, especially considering human conversation dynamics such as interruptions, backchannels, and overlapping speech.
Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization
Then we introduce a joint pairwise constraint propagation algorithm to cluster speakers based on these visual and semantic constraints.
Recording for Eyes, Not Echoing to Ears: Contextualized Spoken-to-Written Conversion of ASR Transcripts
To improve readability, we propose a Contextualized Spoken-to-Written conversion (CoS2W) task to address ASR and grammar errors and also transfer the informal text into the formal style with content preserved, utilizing contexts and auxiliary information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multimodal Fusion and Coherence Modeling for Video Topic Segmentation
In this work, we improve supervised VTS by thoroughly exploring multimodal fusion and multimodal coherence modeling.
FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs
This report introduces FunAudioLLM, a model family designed to enhance natural voice interactions between humans and large language models (LLMs).
Skip-Layer Attention: Bridging Abstract and Detailed Dependencies in Transformers
The Transformer architecture has significantly advanced deep learning, particularly in natural language processing, by effectively managing long-range dependencies.
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems
Based on this, we propose a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system and discusses the corresponding mitigation strategies.
Loss Masking Is Not Needed in Decoder-only Transformer for Discrete-token-based ASR
We find that applying the conventional cross-entropy loss on input speech tokens does not consistently improve the ASR performance over the Loss Masking approach.
Improving Long Document Topic Segmentation Models With Enhanced Coherence Modeling
Our approach improve $F_1$ of old SOTA by 3. 42 (73. 74 -> 77. 16) and reduces $P_k$ by 1. 11 points (15. 0 -> 13. 89) on WIKI-727K and achieves an average relative reduction of 4. 3% on $P_k$ on WikiSection.
Improving Speaker Diarization using Semantic Information: Joint Pairwise Constraints Propagation
Speaker diarization has gained considerable attention within speech processing research community.
Improving BERT with Hybrid Pooling Network and Drop Mask
Transformer-based pre-trained language models, such as BERT, achieve great success in various natural language understanding tasks.
Advancing Precise Outline-Conditioned Text Generation with Task Duality and Explicit Outline Control
Furthermore, we identify an issue of imbalanced utilization of the outline information in the precise outline-conditioned generation, which is ubiquitously observed across fine-tuned models and zero-shot inference models.
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings
Prior studies diagnose the anisotropy problem in sentence representations from pre-trained language models, e. g., BERT, without fine-tuning.
Meeting Action Item Detection with Regularized Context Modeling
We construct and release the first Chinese meeting corpus with manual action item annotations.
Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG)
ICASSP2023 General Meeting Understanding and Generation Challenge (MUG) focuses on prompting a wide range of spoken language processing (SLP) research on meeting transcripts, as SLP applications are critical to improve users' efficiency in grasping important information in meetings.
MUG: A General Meeting Understanding and Generation Benchmark
To prompt SLP advancement, we establish a large-scale general Meeting Understanding and Generation Benchmark (MUG) to benchmark the performance of a wide range of SLP tasks, including topic segmentation, topic-level and session-level extractive summarization and topic title generation, keyphrase extraction, and action item detection.
DopplerBAS: Binaural Audio Synthesis Addressing Doppler Effect
Recently, binaural audio synthesis (BAS) has emerged as a promising research field for its applications in augmented and virtual realities.
PoNet: Pooling Network for Efficient Token Mixing in Long Sequences
We propose a novel Pooling Network (PoNet) for token mixing in long sequences with linear complexity.
Sequence Model with Self-Adaptive Sliding Window for Efficient Spoken Document Segmentation
Evaluations are conducted on the English Wiki-727K document segmentation benchmark, a Chinese Wikipedia-based document segmentation dataset we created, and an in-house Chinese spoken document dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Discriminative Self-training for Punctuation Prediction
Punctuation prediction for automatic speech recognition (ASR) output transcripts plays a crucial role for improving the readability of the ASR transcripts and for improving the performance of downstream natural language processing applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning
In this paper, we propose a novel joint textual-phonetic pre-training approach for learning spoken language representations, aiming at exploring the full potentials of phonetic information to improve SLU robustness to ASR errors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
An Application of ASP Theories of Intentions to Understanding Restaurant Scenarios: Insights and Narrative Corpus
This paper presents a practical application of Answer Set Programming to the understanding of narratives about restaurants.
An ASP Methodology for Understanding Narratives about Stereotypical Activities
We exemplify the application of this methodology by answering questions about a number of restaurant stories.
