6 citations · 7 across the 13 of their papers we have counts for
13 papers
Mandarin Humorous Homophone Recognition and Disambiguation in Automatic Speech Recognition
Sicheng Jin, Jinghao Chen, Liuheng Zhou +3
Mandarin homophones remain a key challenge to improving automatic speech recognition (ASR) accuracy due to the amount of potential homophones. Mandarin speakers use this feature ca…
Using Phonological-Level Wav2Vec2 for Mandarin Automatic Mispronunciation Detection and Diagnosis
Jinghao Chen, Mostafa Shahin, Beena Ahmed
Automatic mispronunciation detection and diagnosis (MDD) plays a crucial role in L2 Mandarin pronunciation learning. While end-to-end (E2E) based MDD methods have substantially imp…
The WER Trap: Shattering the Illusion of Unified Tokens in Speech Language Models
Xiangyu Zhang, Yuxin Li, Haoyang Zhang +5
The pursuit of a "unified" discrete token for both speech understanding and generation has led the Speech Language Model (SLM) community to heavily rely on Word Error Rate (WER) --…
Why Your Tokenizer Fails in Information Fusion: A Timing-Aware Pre-Quantization Fusion for Video-Enhanced Audio Tokenization
Xiangyu Zhang, Benjamin John Southwell, Siqi Pan +3
Audio tokenization has emerged as a critical component in end-to-end audio language models, enabling efficient discrete representation learning for both audio understanding and gen…
System X: A Mobile Voice-Based AI System for EMR Generation and Clinical Decision Support in Low-Resource Maternal Healthcare
Maryam Mustafa, Umme Ammara, Amna Shahnawaz +5
We present the design, implementation, and in-situ deployment of a smartphone-based voice-enabled AI system for generating electronic medical records (EMRs) and clinical risk alert…
Zero-Shot Cognitive Impairment Detection from Speech Using AudioLLM
Mostafa Shahin, Beena Ahmed, Julien Epps
Cognitive impairment (CI) is of growing public health concern, and early detection is vital for effective intervention. Speech has gained attention as a non-invasive and easily col…
Distinctive Feature Codec: An Adaptive Efficient Speech Representation for Depression Detection
Xiangyu Zhang, Fuming Fang, Peng Gao +3
Large Language Models (LLMs) have demonstrated remarkable success across diverse fields, establishing a powerful paradigm for complex information processing. This has inspired the…
Why Pre-trained Models Fail: Feature Entanglement in Multi-modal Depression Detection
Xiangyu Zhang, Beena Ahmed, Julien Epps
Depression remains a pressing global mental health issue, driving considerable research into AI-driven detection approaches. While pre-trained models, particularly speech self-supe…
SpeechT-RAG: Reliable Depression Detection in LLMs with Retrieval-Augmented Generation Using Speech Timing Information
Xiangyu Zhang, Hexin Liu, Qiquan Zhang +2
Large Language Models (LLMs) have been increasingly adopted for health-related tasks, yet their performance in depression detection remains limited when relying solely on text inpu…
Rethinking Mamba in Speech Processing by Self-Supervised Models
Xiangyu Zhang, Jianbo Ma, Mostafa Shahin +2
The Mamba-based model has demonstrated outstanding performance across tasks in computer vision, natural language processing, and speech processing. However, in the realm of speech…
Auto-Landmark: Acoustic Landmark Dataset and Open-Source Toolkit for Landmark Extraction
Xiangyu Zhang, Daijiao Liu, Tianyi Xiao +5
In the speech signal, acoustic landmarks identify times when the acoustic manifestations of the linguistically motivated distinctive features are most salient. Acoustic landmarks h…
Mamba in Speech: Towards an Alternative to Self-Attention
Xiangyu Zhang, Qiquan Zhang, Hexin Liu +6
Transformer and its derivatives have achieved success in diverse tasks across computer vision, natural language processing, and speech processing. To reduce the complexity of compu…
When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection
Xiangyu Zhang, Hexin Liu, Kaishuai Xu +4
Depression is a critical concern in global mental health, prompting extensive research into AI-based detection methods. Among various AI technologies, Large Language Models (LLMs)…