Liu Chen

Liu Chen, Michael Deisher, Munir Georges

This paper describes an end-to-end (E2E) neural architecture for the audio rendering of small portions of display content on low resource personal computing devices. It is intended to address the problem of accessibility for vision-impaired or vision-distracted users at the hardware level. Neural image-to-text (ITT) and text-to-speech (TTS) approaches are reviewed and a new technique is introduced to efficiently integrate them in a way that is both efficient and back-propagate-able, leading to a non-autoregressive E2E image-to-speech (ITS) neural network that is efficient and trainable. Experimental results are presented showing that, compared with the non-E2E approach, the proposed E2E system is 29% faster and uses 19% fewer parameters with a 2% reduction in phone accuracy. A future direction to address accuracy is presented.

Liu Chen, Meysam Asgari

Building a high quality automatic speech recognition (ASR) system with limited training data has been a challenging task particularly for a narrow target population. Open-sourced ASR systems, trained on sufficient data from adults, are susceptible on seniors' speech due to acoustic mismatch between adults and seniors. With 12 hours of training data, we attempt to develop an ASR system for socially isolated seniors (80+ years old) with possible cognitive impairments. We experimentally identify that ASR for the adult population performs poorly on our target population and transfer learning (TL) can boost the system's performance. Standing on the fundamental idea of TL, tuning model parameters, we further improve the system by leveraging an attention mechanism to utilize the model's intermediate information. Our approach achieves 1.58% absolute improvements over the TL model.

Xiaofan Mu, Merna Bibars, Salman Seyedi et al.

The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning (ML) models captured facial, acoustic, linguistic, and cardiovascular features from 39 older adults with normal cognition or Mild Cognitive Impairment (MCI), derived from remote video conversations and quantified their cognitive status, social isolation, neuroticism, and psychological well-being. Our model could distinguish Clinical Dementia Rating Scale (CDR) of 0.5 (vs. 0) with 0.77 area under the receiver operating characteristic curve (AUC), social isolation with 0.74 AUC, social satisfaction with 0.75 AUC, psychological well-being with 0.72 AUC, and negative affect with 0.74 AUC. Our feature importance analysis showed that speech and language patterns were useful for quantifying cognitive impairment, whereas facial expressions and cardiovascular patterns were useful for quantifying social and psychological well-being. Our bias analysis showed that the best-performing models for quantifying psychological well-being and cognitive states in older adults exhibited significant biases concerning their age, sex, disease condition, and education levels. Our comprehensive analysis shows the feasibility of monitoring the cognitive and psychological health of older adults, as well as the need for collecting largescale interview datasets of older adults to benefit from the latest advances in deep learning technologies to develop generalizable models across older adults with diverse demographic backgrounds and disease conditions.

Liu Chen, Meysam Asgari

A deep Transformer model with good evaluation score does not mean each subnetwork (a.k.a transformer block) learns reasonable representation. Diagnosing abnormal representation and avoiding it can contribute to achieving a better evaluation score. We propose an innovative perspective for analyzing attention patterns: summarize block-level patterns and assume abnormal patterns contribute negative influence. We leverage Wav2Vec 2.0 as a research target and analyze a pre-trained model's pattern. All experiments leverage Librispeech-100-clean as training data. Through avoiding diagnosed abnormal ones, our custom Wav2Vec 2.0 outperforms the original version about 4.8% absolute word error rate (WER) on test-clean with viterbi decoding. Our version is still 0.9% better when decoding with a 4-gram language model. Moreover, we identify that avoiding abnormal patterns is the main contributor for performance boosting.