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Yang Ai

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2 papers

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AAAI Conference 2026 Conference Paper

Say More with Less: Variable-Frame-Rate Speech Tokenization via Adaptive Clustering and Implicit Duration Coding

Rui-Chen Zheng
Wenrui Liu
Hui-Peng Du
Qinglin Zhang
Chong Deng
Qian Chen
Wen Wang
Yang Ai

Existing speech tokenizers typically assign a fixed number of tokens per second, regardless of the varying information density or temporal fluctuations in the speech signal. This uniform token allocation mismatches the intrinsic structure of speech, where information is distributed unevenly over time. To address this, we propose VARSTok, a VAriable-frame-Rate Speech Tokenizer that adapts token allocation based on local feature similarity. VARSTok introduces two key innovations: (1) a temporal-aware density peak clustering algorithm that adaptively segments speech into variable-length units, and (2) a novel implicit duration coding scheme that embeds both content and temporal span into a single token index, eliminating the need for auxiliary duration predictors. Extensive experiments show that VARSTok significantly outperforms strong fixed-rate baselines. Notably, it achieves superior reconstruction naturalness while using up to 23% fewer tokens than a 40 Hz fixed-frame-rate baseline. VARSTok further yields lower word error rates and improved naturalness in zero-shot text-to-speech synthesis. To the best of our knowledge, this is the first work to demonstrate that a fully dynamic, variable-frame-rate acoustic speech tokenizer can be seamlessly integrated into downstream speech language models.

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JBHI Journal 2025 Journal Article

SAMA: A Self-and-Mutual Attention Network for Accurate Recurrence Prediction of Non-Small Cell Lung Cancer Using Genetic and CT Data

Yang Ai
Jing Liu
Yinhao Li
Fang Wang
Xiuju Du
Rahul Kumar Jain
Lanfen Lin
Yen-Wei Chen

Accurate preoperative recurrence prediction for non-small cell lung cancer (NSCLC) is a challenging issue in the medical field. Existing studies primarily conduct image and molecular analyses independently or directly fuse multimodal information through radiomics and genomics, which fail to fully exploit and effectively utilize the highly heterogeneous cross-modal information at different levels and model the complex relationships between modalities, resulting in poor fusion performance and becoming the bottleneck of precise recurrence prediction. To address these limitations, we propose a novel unified framework, the Self-and-Mutual Attention (SAMA) Network, designed to efficiently fuse and utilize macroscopic CT images and microscopic gene data for precise NSCLC recurrence prediction, integrating handcrafted features, deep features, and gene features. Specifically, we design a Self-and-Mutual Attention Module that performs three-stage fusion: the self-enhancement stage enhances modality-specific features; the gene-guided and CT-guided cross-modality fusion stages perform bidirectional cross-guidance on the self-enhanced features, complementing and refining each modality, enhancing heterogeneous feature expression; and the optimized feature aggregation stage ensures the refined interactive features for precise prediction. Extensive experiments on both publicly available datasets from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) demonstrate that our method achieves state-of-the-art performance and exhibits broad applicability to various cancers.

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