Keywords:
- Aho-Corasick algorithm
- ASR
- ASR robustness
- Automatic Depression Detection
- automatic speech recognition (ASR)
- Benchmark
- Benchmarking
- bias
- bias aware
- bias word error rate
- Clinical Interviews
- contact-center speech
- contextual ASR
- Contextualisation and adaptation of ASR
- conversational agents
- conversational AI
- conversational hallucination
- conversational modeling
- Data Selection
- dataset
- Depression Corpora
- depression detection
- dialogue evaluation
- dialogue flow
- dialogue simulation
- digital mental health
- domain adaptation
- Domain Classification
- Dual mode encoder
- embedding
- entity linking
- entity-aware evaluation
- explainability
- fact checking
- factual reporting
- flow-aware metrics
- Foundation Models
- Graph Convolutional Network (GCN)
- Graph Convolutional Networks
- Graph Neural Networks
- hallucination
- hallucination detection
- information verification
- Interpretability
- Interpretable Models
- language identification
- Language Production
- Large Language Models
- limited training data
- LLM
- LLM evaluation
- LLM-based ASR
- low-resource
- media bias
- Mental Lexicon
- multitask training
- named entity recognition
- Natural language processing
- news media
- node weighted graphs
- orchestration
- personas
- procedural hallucination
- procedural similarity
- prompt projection
- pseudo-labelling
- real-time ASR
- reinforcement learning
- reliability estimation
- reproducibility
- resources and evaluation
- scenario management
- sentence embeddings
- shallow fusion
- Speaker change detection
- speech recognition
- Speech-to-LLM alignment
- speech-to-text alignment
- spoken dialogue systems
- streaming ASR
- streaming transducer
- synthetic dialogue
- task-oriented dialog
- task-oriented dialogue
- Text classification
- text denoising
- Text fine-tuning
- topic modelling
- transformer transducer
- user identity linkage
- whisper
- XLSR-Transducer
- Zipformer
Publications of Sergio Burdisso sorted by recency
| 1 | 2 |
| Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews, , , and , Idiap-RR-03-2023 |
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| IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model, , , , , , and , in: The 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE @ EMNLP 2022), 2022 |
[URL] |
| IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach, , , , , , and , in: The 5th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE @ EMNLP 2022), 2022 |
[URL] |
| Two Simple and Domain-independent Approaches for Early Detection of Anorexia, , , and , in: Early Detection of Mental Health Disorders by Social Media Monitoring: The First Five Years of the eRisk Project, pages 159-182, Springer International Publishing, 2022 |
[DOI] [URL] |
| UNSL at eRisk 2022: Decision policies with history for early classification, , , and , in: CEUR Workshop Proceedings, 2022 |
[URL] |
| IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model, , , , , , and , Idiap-RR-12-2022 |
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| IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach, , , , , , and , Idiap-RR-13-2022 |
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| 1 | 2 |