17 accepted papers
Backdoor data poisoning is a crucial technique for ownership protection and defending against malicious attacks. Embedding hidden triggers in training data can manipulate model outputs, enabling provenance verification, and deterring unauthorized use. However, current audio backdoor methods are subo…
Text embeddings enable numerous NLP applications but face severe privacy risks from embedding inversion attacks, which can expose sensitive attributes or reconstruct raw text. Existing differential privacy defenses assume uniform sensitivity across embedding dimensions, leading to excessive noise an…
Instruction-guided text-to-speech (ITTS) enables users to control speech generation through natural language prompts, offering a more intuitive interface than traditional TTS. However, the alignment between user style instructions and listener perception remains largely unexplored. This work first p…
Existing large language model (LLM)-based embeddings typically adopt an encoder-only paradigm, treating LLMs as static feature extractors and overlooking their core gener- ative strengths. We introduce GIRCSE (Generative Iterative Refinement for Contrastive Sentence Embeddings), a novel framework th…
Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts. While existing surveys provide comprehensive overviews of MAS infrastructures, they largely overlook the dimension of creativity , including how novel outputs
Deep Neural Networks (DNNs) have revolutionized artificial intelligence, achieving impressive results on diverse data types, including images, videos, and texts. However, DNNs still lag behind Gradient Boosting Decision Trees (GBDT) on tabular data, a format extensively utilized across various domai…
Tabular data is arguably one of the most commonly used data structures in various practical domains, including finance, healthcare and e-commerce. The inherent heterogeneity allows tabular data to store rich information. However, based on a recently published tabular benchmark, we can see deep neura…
We propose a simple method for automatic speech recognition (ASR) by fine-tuning BERT, which is a language model (LM) trained on large-scale unlabeled text data and can generate rich contextual representations. Our assumption is that given a history context sequence, a powerful LM can narrow the ran…
Recent developments in deep learning have led to a significant innovation in various classic and practical subjects, including speech recognition, computer vision, question answering, information retrieval and so on. In the context of natural language processing (NLP), language representations learn…
Spoken document retrieval (SDR) has become an important research subject due to the immenseness of multimedia data along with speech have spread around the world in our daily life. One of the fundamental challenge facing SDR is that the input query usually contains only a few words, which is too sho…
Spoken document retrieval (SDR) has become a prominently required application since unprecedented volumes of multimedia data along with speech have become available in our daily life. As far as we are aware, there has been relatively less work in launching unsupervised paragraph embedding methods an…
Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. Some research works trying to modify the sentiment of the output sequences were reported. In this paper, we…
Because unprecedented volumes of multimedia data associated with spoken documents have been made available to the public, spoken document retrieval (SDR) has become an important research area in the past decades. Recently, representation learning has emerged as an active research topic in many machi…
Extractive speech summarization is intended to produce a condensed version of the original spoken document by selecting a few salient sentences from the document and concatenate them together to form a summary. In this paper, we study a novel use of manifold learning techniques for extractive speech…
In this paper, we present an efficient framework for human activity recognition in daily environment. We use depth information mainly for privacy protection, and then focus on the motion analysis of informative body parts, since most activities are much associated with these particular parts, e.g.,
Extractive summarization aims at selecting a set of indicative sentences from a source document as a summary that can express the major theme of the document. A general consensus on extractive summarization is that both relevance and coverage are critical issues to address. The existing methods desi…
Since more and more multimedia data associated with spoken documents have been made available to the public, spoken document retrieval (SDR) has become an important research subject in the past two decades. Following the research tendency, many efforts have been devoted towards developing indexing a…