Papers by Peter Bell
Improving Code-switched ASR with Linguistic Information (2022.coling-1)
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| Challenge: | Existing studies on code-switching have been limited to the individual languages, but the results are promising. |
| Approach: | They propose to apply linguistic theories to generate more realistic code-switching text, which is needed for language modelling in ASR. |
| Outcome: | The proposed system improves 2% on English-Spanish code-switching . Equivalence Constraint theory and part-of-speech labelling are particularly helpful for text generation and bring 2% improvement to ASR performance. |
Do dialogue representations align with perception? An empirical study (2023.eacl-main)
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| Challenge: | masked language models produce stronger correlations than auto-regressive models, but humans and models make different response selection mistakes. |
| Approach: | They propose to use spoken conversation as a model to measure human comprehension behaviour. |
| Outcome: | The proposed model outperforms the model which produces the strongest correlation with human responses. |
Analyzing the Role of Part-of-Speech in Code-Switching: A Corpus-Based Study (2024.findings-eacl)
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| Challenge: | Code-switching (CS) is a common linguistic phenomenon wherein speakers fluidly transition between languages in conversation. |
| Approach: | They propose to use a part-of-speech (POS)-based analysis of Spanish-English and Mandarin-English corpora to examine the propensity of bilinguals to engage in CS. |
| Outcome: | The findings confirm the existence of a statistically significant connection between POS and the likelihood of CS across language pairs, but show that it diminishes as tokens distance themselves from CS instances. |
Analysing the role of lexical and temporal information in turn-taking through predictability (2026.eacl-long)
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| Challenge: | Existing evaluations of spoken dialogue systems do not address which information sources drive predictions. |
| Approach: | They examine the role of lexical-temporal features on the predictability of turn structure by examining PairwiseTurnGPT, a full-duplex model of spoken dialogue transcripts. |
| Outcome: | The proposed model can produce fluent conversational output, but it does not guarantee realistic turn-taking behaviour. |
Spoken Document Retrieval for an Unwritten Language: A Case Study on Gormati (2025.findings-emnlp)
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Sanjay Booshanam, Kelly Chen, Ondrej Klejch, Thomas Reitmaier, Dani Kalarikalayil Raju, Electra Wallington, Nina Markl, Jennifer Pearson, Matt Jones, Simon Robinson, Peter Bell
| Challenge: | Speakers of unwritten languages have the potential to benefit from speech-based automatic information retrieval systems. |
| Approach: | They propose a speech embedding technique that facilitates a zero-shot speech-based automatic information retrieval system for unwritten languages. |
| Outcome: | The proposed method achieves a Top 5 retrieval rate of 87.9% on a corpus of Gormati, an unwritten language, that was collected in partnership with an agrarian Banjara community in Maharashtra State, India. |
Segmenting Subtitles for Correcting ASR Segmentation Errors (2021.eacl-main)
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David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuscakova, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
| Challenge: | Typical ASR systems segment input audio into utterances using purely acoustic information, which may not resemble sentence-like units expected by conventional machine translation systems for spoken language translation (SLT). |
| Approach: | They propose a model for correcting ASR acoustic segmentation using subtitles as a proxy dataset for creating synthetic aural utterances by modeling common error modes. |
| Outcome: | The proposed model improves performance on MT and audio-document cross-language information retrieval (CLIR) it uses subtitles as a proxy dataset to correct ASR acoustic segmentation . |
LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots (2025.coling-main)
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| Challenge: | Large language models have shown significant potential for robotics tasks, but a gap remains in personalization of LLMs to household preferences. |
| Approach: | They propose a framework to personalize LLM planners for household robotics . they use imitation learning and reinforced self-training to personalise the planner . |
| Outcome: | The proposed framework performs iterative planning in multi-room, partially-observable household environments, utilizing a scene graph built dynamically from local observations. |