Identifying cognitive impairment using sentence representation vectors
(3 minutes introduction)
| Bahman Mirheidari (University of Sheffield, UK), Yilin Pan (University of Sheffield, UK), Daniel Blackburn (University of Sheffield, UK), Ronan O’Malley (University of Sheffield, UK), Heidi Christensen (University of Sheffield, UK) |
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The widely used word vectors can be extended at the sentence level to perform a wide range of natural language processing (NLP) tasks. Recently the Bidirectional Encoder Representations from Transformers (BERT) language representation achieved state-of-the-art performance for these applications. The model is trained with punctuated and well-formed (writ-ten) text, however, the performance of the model drops significantly when the input text is the — erroneous and unpunctuated — output of automatic speech recognition (ASR). We use a sliding window and averaging approach for pre-processing text for BERT to extract features for classifying three diagnostic categories relating to cognitive impairment: neurodegenerative dis-order (ND), mild cognitive impairment (MCI), and healthy controls (HC). The in-house dataset contains the audio recordings of an intelligent virtual agent (IVA) who asks the participants several conversational questions prompts in addition to giving a picture description prompt. For the three-way classification, we achieve a 73.88% F-score (accuracy: 76.53%) using the pre-trained, uncased base BERT and for the two-way classifier (HC vs. ND) we achieve 89.80% (accuracy: 90%). We further improve these by using a prompt selection technique, reaching the F-scores of 79.98% (accuracy: 81.63%) and 93.56% (accuracy: 93.75%) respectively.
