SE4Lip: Speech-Lip Encoder for Talking Head Synthesis to Solve Phoneme-Viseme Alignment Ambiguity

Speech-driven talking head synthesis tasks commonly use general acoustic features (such as HuBERT and DeepSpeech) as guided speech features. However, we discovered that these features suffer from phoneme-viseme alignment ambiguity, which refers to the uncertainty and imprecision in matching phonemes (speech) with visemes (lip). To address this issue, we propose the Speech Encoder for Lip (SE4Lip) to encode lip features from speech directly, aligning speech and lip features in the joint embedding space by a cross-modal alignment framework. The STFT spectrogram with the GRU-based model is designed in SE4Lip to preserve the fine-grained speech features. Experimental results show that SE4Lip achieves state-of-the-art performance in both NeRF and 3DGS rendering models. Its lip sync accuracy improves by 13.7% and 14.2% compared to the best baseline and produces results close to the ground truth videos.

Analysis of Legal Documents via Non-negative Matrix Factorization Methods

The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assistance and corresponding case files. Processing and interpreting this large amount of information presents a significant challenge for CIP officials, which can be successfully aided by topic modeling techniques.In this paper, we apply Non-negative Matrix Factorization (NMF) method and implement various offshoots of it to the important and previously unstudied data set compiled by CIP. We identify underlying topics of existing case files and classify request files by crime type and case status (decision type). The results uncover the semantic structure of current case files and can provide CIP officials with a general understanding of newly received case files before further examinations. We also provide an exposition of popular variants of NMF with their experimental results and discuss the benefits and drawbacks of each variant through the real-world application.

COVID-19 Literature Topic-Based Search via Hierarchical NMF

A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics. We discover eight major latent topics and 52 granular subtopics in the body of literature, related to vaccines, genetic structure and modeling of the disease and patient studies, as well as related diseases and virology. In order that our tool may help current researchers, an interactive website is created that organizes available literature using this hierarchical structure.