Papers by Phil Woodland
DNCASR: End-to-End Training for Speaker-Attributed ASR (2025.acl-long)
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| Challenge: | DNCASR is a novel end-to-end trainable system for speaker clustering and automatic speech recognition . it is designed to transcribe long multi-party meetings with speaker-attributed transcriptions . |
| Approach: | They propose a joint neural speaker clustering and automatic speech recognition system . DNCASR uses two separate encoders to independently encode speaker characteristics and local waveform information . |
| Outcome: | The proposed system outperforms a parallel system that does not have links between speaker and ASR decoders. |
Protecting Bystander Privacy via Selective Hearing in Audio LLMs (2026.acl-long)
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| Challenge: | Audio Large language models capture speech from unintended bystanders, raising privacy risks that existing benchmarks and defences did not consider. |
| Approach: | They propose to evaluate selective hearing by evaluating a model’s ability to attend to an intended main speaker while refusing to process or reveal information about incidental bystander speech. |
| Outcome: | The proposed model can attend to an intended main speaker while refusing to process or reveal information about incidental bystander speech. |
Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation (2024.acl-long)
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| Challenge: | Experimental results show that incorporating utterances without majority-agreed labels into an additional class reduces the classification performance of the other emotion classes. |
| Approach: | They propose to combine utterances without majority-agreed labels into an additional class . they propose to quantify uncertainty in emotion classification using evidential deep learning . |
| Outcome: | The proposed method retains classification accuracy while effectively detects ambiguous emotion expressions. |
Speech-based Slot Filling using Large Language Models (2024.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have shown an unprecedented ability across various language tasks. |
| Approach: | They propose to use prompts and LoRA fine-tuning to improve slot filling robustness . they propose a linearised knowledge injection scheme to integrate dynamic external knowledge into LLMs. |
| Outcome: | The proposed model improves slot filling with noisy ASR transcriptions with 6.7% and 17.6% absolute SLU-F1 improvements compared to a fully fine-tuned Flan-T5-XL model. |
Label-Synchronous Neural Transducer for E2E Simultaneous Speech Translation (2024.acl-long)
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| Challenge: | LS-Transducer-SST dynamically decides when to emit translation tokens based on an Auto-regressive Integrate-and-Fire (AIF) mechanism. |
| Approach: | They propose a label-synchronous neural transducer for simultaneous speech translation which dynamically decides when to emit translation tokens based on an Auto-regressive Integrate-and-Fire mechanism. |
| Outcome: | The proposed model can predict translation tokens on monolingual text-only data or can be used in both decoding and training. |
Modelling Variability in Human Annotator Simulation (2024.findings-acl)
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| Challenge: | Human annotator simulation (HAS) is a cost-effective alternative to human evaluation tasks. |
| Approach: | They propose a framework to model human annotation variability via meta-learning . conditional softmax flow model leverages diverse human annotations via meta learning . results demonstrate that method can predict aggregated behaviours of human annotators . |
| Outcome: | The proposed method achieves state-of-the-art performance on two real-world human evaluation tasks: emotion recognition and toxic speech detection. |
SkillAggregation: Reference-free LLM-Dependent Aggregation (2025.acl-long)
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| Challenge: | Existing methods in NLP assign equal weight to all LLM judgments or are designed for specific tasks such as hallucination detection. |
| Approach: | They propose a method that learns to combine LLM judgments without additional data or ground truth to exploit the judge estimates during inference. |
| Outcome: | The proposed method outperforms Crowdlayer on all tasks and yields the best performance over all approaches on the majority of tasks. |
Wav2Prompt: End-to-End Speech Prompt Learning and Task-based Fine-tuning for Text-based LLMs (2025.naacl-long)
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| Challenge: | Text-based large language models (LLMs) can be applied to a wide range of tasks without being explicitly trained. |
| Approach: | They propose a method which integrates spoken input with a text-based large language model (LLM) it takes LLM token embeddings as training targets and utilises a continuous integrate-and-fire mechanism for explicit speech-text alignment. |
| Outcome: | The proposed model can be applied to speech translation, speech understanding and spoken-query-based question answering tasks. |
SimulS2S-LLM: Unlocking Simultaneous Inference of Speech LLMs for Speech-to-Speech Translation (2025.acl-long)
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| Challenge: | Simultaneous speech translation (SST) outputs translations in parallel with streaming speech input, balancing translation quality and latency. |
| Approach: | They propose to train LLMs offline and employ a test-time policy to guide simultaneous inference by extracting boundary-aware speech prompts that allow it to be better matched with text input data. |
| Outcome: | The proposed model trains speech LLMs offline and employs a test-time policy to guide simultaneous inference. |