Automated lip-reading for improved speech intelligibility

McClain, Matthew; Brady, Kevin; Brandstein, M.S.; Quatieri, Thomas F.

doi:10.1109/icassp.2004.1326082

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…17 This contribution is most pronounced in noisy environments, where the intelligibility of audio-only speech is quickly degraded. 25 We recommend that regular reminders to speak up and acknowledge communication difficulties at key times during intensive care unit ward rounds and operating theatre presurgery briefs may help staff to improve communication whilst wearing PPE. Some specialty guidelines have recommended staff members wearing photographs of themselves over their PPE, or writing their name and roles on the apron.…”

Section: Discussionmentioning

confidence: 99%

The negative impact of wearing personal protective equipment on communication during coronavirus disease 2019

et al. 2020

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Background Coronavirus disease 2019 personal protective equipment has been reported to affect communication in healthcare settings. This study sought to identify those challenges experimentally. Method Bamford–Kowal–Bench speech discrimination in noise performance of healthcare workers was tested under simulated background noise conditions from a variety of hospital environments. Candidates were assessed for ability to interpret speech with and without personal protective equipment, with both normal speech and raised voice. Results There was a significant difference in speech discrimination scores between normal and personal protective equipment wearing subjects in operating theatre simulated background noise levels (70 dB). Conclusion Wearing personal protective equipment can impact communication in healthcare environments. Efforts should be made to remind staff about this burden and to seek alternative communication paradigms, particularly in operating theatre environments.

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Section: Discussionmentioning

confidence: 99%

The negative impact of wearing personal protective equipment on communication during coronavirus disease 2019

et al. 2020

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Comparison of colour transforms used in lip segmentation algorithms

Gritzman

Rubin

Pantanowitz

2014

SIViP

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Lip-Reading via Deep Neural Networks Using Hybrid Visual Features

2018

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Lip-reading is typically known as visually interpreting the speaker's lip movements during speaking. Experiments over many years have revealed that speech intelligibility increases if visual facial information becomes available. This effect becomes more apparent in noisy environments. Taking steps toward automating this process, some challenges will be raised such as coarticulation phenomenon, visual units' type, features diversity and their inter-speaker dependency. While efforts have been made to overcome these challenges, presentation of a flawless lip-reading system is still under the investigations. This paper searches for a lipreading model with an efficiently developed incorporation and arrangement of processing blocks to extract highly discriminative visual features. Here, application of a properly structured Deep Belief Network (DBN)- based recognizer is highlighted. Multi-speaker (MS) and speaker-independent (SI) tasks are performed over CUAVE database, and phone recognition rates (PRRs) of 77.65% and 73.40% are achieved, respectively. The best word recognition rates (WRRs) achieved in the tasks of MS and SI are 80.25% and 76.91%, respectively. Resulted accuracies demonstrate that the proposed method outperforms the conventional Hidden Markov Model (HMM) and competes well with the state-of-the-art visual speech recognition works.

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Automated lip-reading for improved speech intelligibility

Cited by 3 publications

References 7 publications

The negative impact of wearing personal protective equipment on communication during coronavirus disease 2019

The negative impact of wearing personal protective equipment on communication during coronavirus disease 2019

Comparison of colour transforms used in lip segmentation algorithms

Lip-Reading via Deep Neural Networks Using Hybrid Visual Features

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