arXiv subjects

Yi-Chen Wu

Publications and source records attributed to Yi-Chen Wu.

5 recordsLinked to original sources

arXiv2025

Unsymmetrical synthesis of benzimidazole-fused naphthalene imides with panchromatic absorption and redox activity

We report a concise synthesis of unsymmetrical benzimidazole-fused naphthalene imide (BfNI) and anhydride (BfNA) derivatives featuring broad UV-Vis-NIR absorption, stable redox activity, and enhanced solubility. Incorporation of triarylamine donors induces strong intramolecular charge transfer and narrows the optical bandgap. This modular design bypasses multistep protection-deprotection and complex pi-assembly, offering a versatile platform for tunable optoelectronic materials.

physics.chem-ph↗

arXiv2025

FireRedTTS-1S: An Upgraded Streamable Foundation Text-to-Speech System

In this work, we upgrade FireRedTTS to a new version, FireRedTTS-1S, a high-quality streaming foundation text-to-speech system. FireRedTTS-1S achieves streaming speech generation via two steps: text-to-semantic decoding and semantic-to-acoustic decoding. In text-to-semantic decoding, a semantic-aware speech tokenizer converts the speech signal into semantic tokens, which can be synthesized from the text via a language model in an auto-regressive manner. Meanwhile, the semantic-to-acoustic decoding module simultaneously translates generated semantic tokens into the speech signal in a streaming way. We implement two approaches to achieve this module: 1) a chunk-wise streamable flow-matching approach, and 2) a multi-stream language model-based approach. They both present high-quality and streamable speech generation but differ in real-time factor (RTF) and latency. Specifically, flow-matching decoding can generate speech by chunks, presenting a lower RTF of 0.1 but a higher latency of 300ms. Instead, the multi-stream language model generates speech by frames in an autoregressive manner, presenting a higher RTF of 0.3 but a low latency of 150ms. In experiments on zero-shot voice cloning, the objective results validate FireRedTTS-1S as a high-quality foundation model with comparable intelligibility and speaker similarity over industrial baseline systems. Furthermore, the subjective score of FireRedTTS-1S highlights its impressive synthesis performance, achieving comparable quality to the ground-truth recordings. These results validate FireRedTTS-1S as a high-quality streaming foundation TTS system.

cs.SD↗

arXiv2025

FireRedTTS: A Foundation Text-To-Speech Framework for Industry-Level Generative Speech Applications

This work proposes FireRedTTS, a foundation text-to-speech framework, to meet the growing demands for personalized and diverse generative speech applications. The framework comprises three parts: data processing, foundation system, and downstream applications. First, we comprehensively present our data processing pipeline, which transforms massive raw audio into a large-scale high-quality TTS dataset with rich annotations and a wide coverage of content, speaking style, and timbre. Then, we propose a language-model-based foundation TTS system. The speech signal is compressed into discrete semantic tokens via a semantic-aware speech tokenizer, and can be generated by a language model from the prompt text and audio. Then, a two-stage waveform generator is proposed to decode them to the high-fidelity waveform. We present two applications of this system: voice cloning for dubbing and human-like speech generation for chatbots. The experimental results demonstrate the solid in-context learning capability of FireRedTTS, which can stably synthesize high-quality speech consistent with the prompt text and audio. For dubbing, FireRedTTS can clone target voices in a zero-shot way for the UGC scenario and adapt to studio-level expressive voice characters in the PUGC scenario via few-shot fine-tuning with 1-hour recording. Moreover, FireRedTTS achieves controllable human-like speech generation in a casual style with paralinguistic behaviors and emotions via instruction tuning, to better serve spoken chatbots.

cs.SD↗

arXiv2025

Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson's Disease Stage Prediction

Parkinson's disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques such as single-photon emission computed tomography (SPECT). In this study, we utilized two SPECT data sets (n = 634 and n = 202) from different hospitals to develop a model capable of accurately predicting PD stages, a multiclass classification task. We used the entire three-dimensional (3D) brain images as input and experimented with various model architectures. Initially, we treated the 3D images as sequences of two-dimensional (2D) slices and fed them sequentially into 2D convolutional neural network (CNN) models pretrained on ImageNet, averaging the outputs to obtain the final predicted stage. We also applied 3D CNN models pretrained on Kinetics-400. Additionally, we incorporated an attention mechanism to account for the varying importance of different slices in the prediction process. To further enhance model efficacy and robustness, we simultaneously trained the two data sets using weight sharing, a technique known as cotraining. Our results demonstrated that 2D models pretrained on ImageNet outperformed 3D models pretrained on Kinetics-400, and models utilizing the attention mechanism outperformed both 2D and 3D models. The cotraining technique proved effective in improving model performance when the cotraining data sets were sufficiently large.

cs.LG↗

arXiv2024

Unveiling the Role of Lewis Base Strength in Small-Molecule Passivation of Defect Perovskites

Perovskite materials are highly promising for a range of optoelectronic applications including energy conversion technologies, owing to their high charge-carrier mobilities, adaptability of bandgap tuning, and exceptional light-harvesting capabilities. Yet, defects that arise during manufacturing often lead to performance limitations such as hindered efficiency and stability. This is primarily due to significant deviations in crystal geometry and band structure elements such as the Fermi level, work function, and density of states, compared to pristine perovskite. To mitigate these issues, this study explored the passivation of surface iodide-vacancy defect in perovskite using small-molecule Lewis bases, an approach aims to counteract these detrimental effects. Among the examined N-, P- and O-coordinated benzyl derivatives, those featuring a phosphonic acid group as a passivator for the undercoordinated Pb(II) sites demonstrated outstanding electronic structure properties. This was notably achieved by lowering the Fermi level, increasing the work function, and suppressing surface trap states. The effective restoration of electronic properties achieved by targeted small molecule passivation provides crucial insights into enhanced functionality and efficiency for defect perovskite materials.

physics.chem-ph↗