4 citations · 7 across the 14 of their papers we have counts for
16 papers
A Tutorial on Prompt Engineering: From Messy Thoughts to AI Workflows
Erfan Loweimi, Hadi Daneshvar, Samira Loveymi +4
This paper treats prompt engineering as a discipline for turning informal human intent into structured AI work specifications. It develops the practice as a sequence of reusable de…
Towards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment
Xiaoliang Wu, Qiyang Sun, Yupei Li +3
Dysarthria severity assessment is essential for therapy planning and longitudinal monitoring, yet manual perceptual rating is time-consuming and variable across clinicians. Althoug…
Phonetic Error Analysis of Raw Waveform Acoustic Models
Erfan Loweimi, Zhengjun Yue, Andrea Carmantini +3
We analyse error patterns of raw waveform acoustic models on TIMIT phone recognition beyond the overall phone error rate (PER). PER is decomposed across three broad phonetic class…
To Be Multimodal or Not to Be: Query-Adaptive Audio-Visual Person Retrieval via Active Modality Detection
Erfan Loweimi, Mengjie Qian, Kate Knill +7
When retrieving a person from a video archive by voice and face, should the system be multimodal or not? In real-world broadcast archives, unlike curated benchmarks, a target may b…
Predicting Psychological Well-Being from Spontaneous Speech using LLMs
Erfan Loweimi, Sofia de la Fuente Garcia, Saturnino Luz
We investigate the use of Large Language Models (LLMs) for zero-shot prediction of Ryff Psychological Well-Being (PWB) scores from spontaneous speech. Using a few minutes of voice…
Can We Trust LLMs for Mental Health Screening? Consistency, ASR Robustness, and Evidence Faithfulness
Erfan Loweimi, Sofia de la Fuente Garcia, Samira Loveymi +2
LLMs can estimate Hospital Anxiety and Depression Scale (HADS) scores from speech in a zero-shot manner, but clinical deployment requires reliability across three dimensions: intra…
Probing Whisper for Dysarthric Speech in Detection and Assessment
Zhengjun Yue, Devendra Kayande, Zoran Cvetkovic +1
Large-scale end-to-end models such as Whisper have shown strong performance on diverse speech tasks, but their internal behavior on pathological speech remains poorly understood. U…
Speaker Retrieval in the Wild: Challenges, Effectiveness and Robustness
Erfan Loweimi, Mengjie Qian, Kate Knill +1
There is a growing abundance of publicly available or company-owned audio/video archives, highlighting the increasing importance of efficient access to desired content and informat…
Phonetic Error Analysis of Raw Waveform Acoustic Models with Parametric and Non-Parametric CNNs
Erfan Loweimi, Andrea Carmantini, Peter Bell +2
In this paper, we analyse the error patterns of the raw waveform acoustic models in TIMIT's phone recognition task. Our analysis goes beyond the conventional phone error rate (PER)…
Zero-shot Audio Topic Reranking using Large Language Models
Mengjie Qian, Rao Ma, Adian Liusie +3
Multimodal Video Search by Examples (MVSE) investigates using video clips as the query term for information retrieval, rather than the more traditional text query. This enables far…
RCT: Random Consistency Training for Semi-supervised Sound Event Detection
Nian Shao, Erfan Loweimi, Xiaofei Li
Sound event detection (SED), as a core module of acoustic environmental analysis, suffers from the problem of data deficiency. The integration of semi-supervised learning (SSL) lar…
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers
Shucong Zhang, Cong-Thanh Do, Rama Doddipatla +3
Although the lower layers of a deep neural network learn features which are transferable across datasets, these layers are not transferable within the same dataset. That is, in gen…
On the Usefulness of Self-Attention for Automatic Speech Recognition with Transformers
Shucong Zhang, Erfan Loweimi, Peter Bell +1
Self-attention models such as Transformers, which can capture temporal relationships without being limited by the distance between events, have given competitive speech recognition…
Stochastic Attention Head Removal: A simple and effective method for improving Transformer Based ASR Models
Shucong Zhang, Erfan Loweimi, Peter Bell +1
Recently, Transformer based models have shown competitive automatic speech recognition (ASR) performance. One key factor in the success of these models is the multi-head attention…
When Can Self-Attention Be Replaced by Feed Forward Layers?
Shucong Zhang, Erfan Loweimi, Peter Bell +1
Recently, self-attention models such as Transformers have given competitive results compared to recurrent neural network systems in speech recognition. The key factor for the outst…
Acoustic Model Adaptation from Raw Waveforms with SincNet
Joachim Fainberg, Ondřej Klejch, Erfan Loweimi +2
Raw waveform acoustic modelling has recently gained interest due to neural networks' ability to learn feature extraction, and the potential for finding better representations for a…