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1. Effects of face masks on speech recognition in multi-talker babble noise

   https://doi.org/10.1371/journal.pone.0246842  

   Toscano, Joseph C.; Toscano, Cheyenne M. (February 2021, PLOS ONE)

   Fu, Qian-Jie (Ed.)

   Face masks are an important tool for preventing the spread of COVID-19. However, it is unclear how different types of masks affect speech recognition in different levels of background noise. To address this, we investigated the effects of four masks (a surgical mask, N95 respirator, and two cloth masks) on recognition of spoken sentences in multi-talker babble. In low levels of background noise, masks had little to no effect, with no more than a 5.5% decrease in mean accuracy compared to a no-mask condition. In high levels of noise, mean accuracy was 2.8-18.2% lower than the no-mask condition, but the surgical mask continued to show no significant difference. The results demonstrate that different types of masks generally yield similar accuracy in low levels of background noise, but differences between masks become more apparent in high levels of noise. 

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2. Belief and Cognitive State in Text and Speech

   Muzaku, John (May 2025, ERIC)

   This thesis investigates the computational modeling of belief and related cognitive states as expressed in text and speech. Understanding how speakers or authors convey commitment, certainty, and emotions is crucial for language understanding, yet poses significant challenges for current NLP systems. We present a comprehensive study spanning multiple facets of belief prediction. We begin by re-examining the widely used FactBank corpus, correcting a critical projection error and establishing new state-of-the-art results for author-only belief prediction through multi-task learning and error analysis. We then tackle the more complex task of source-and-target belief prediction, introducing a novel generative framework using Flan-T5. This includes developing a structured database representation for FactBank and proposing a linearized tree generation approach, culminating in the BeLeaf system for visualization and analysis, which achieves state-of-the-art performance on both FactBank and the MDP corpus. With the rise of large language models (LLMs), we investigate their zero-shot capabilities for the source-and-target belief task. We propose Unified and Hybrid prompting frameworks, finding that while current LLMs struggle, particularly with nested beliefs, our Hybrid approach paired with reasoning-focused LLMs achieves new state-of-the-art results on FactBank. Finally, we explore the role of multimodality among multiple cognitive states. We present the first study on multimodal belief prediction using the CB-Prosody corpus, demonstrating that integrating audio features via fine-tuned Whisper models significantly improves performance over text-only BERT models. We further introduce Synthetic Audio Data (SAD), showing that even synthetic audio generated by TTS systems provides orthogonal, beneficial signals for various cognitive state tasks (belief, emotion, sentiment). We conclude by presenting OmniVox, the first systematic evaluation of omni-LLMs for zero-shot emotion recognition directly from audio, demonstrating their competitiveness with fine-tuned models and analyzing their acoustic reasoning capabilities. 

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3. Enhancing Resilience to Missing Data in Audio-Text Emotion Recognition with Multi-Scale Chunk Regularization

   https://doi.org/10.1145/3577190.3614110  

   Lin, Wei-Cheng; Goncalves, Lucas; Busso, Carlos (October 2023, ACM)

   na (Ed.)

   Most existing audio-text emotion recognition studies have focused on the computational modeling aspects, including strategies for fusing the modalities. An area that has received less attention is understanding the role of proper temporal synchronization between the modalities in the model performance. This study presents a transformer-based model designed with a word-chunk concept, which offers an ideal framework to explore different strategies to align text and speech. The approach creates chunks with alternative alignment strategies with different levels of dependency on the underlying lexical boundaries. A key contribution of this study is the multi-scale chunk alignment strategy, which generates random alignments to create the chunks without considering lexical boundaries. For every epoch, the approach generates a different alignment for each sentence, serving as an effective regularization method for temporal dependency. Our experimental results based on the MSP-Podcast corpus indicate that providing precise temporal alignment information to create the audio-text chunks does not improve the performance of the system. The attention mechanisms in the transformer-based approach are able to compensate for imperfect synchronization between the modalities. However, using exact lexical boundaries makes the system highly vulnerable to missing modalities. In contrast, the model trained with the proposed multi-scale chunk regularization strategy using random alignment can significantly increase its robustness against missing data and remain effective, even under a single audio-only emotion recognition task. The code is available at: https://github.com/winston-lin-wei-cheng/MultiScale-Chunk-Regularization 

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4. Effects of experience on recognition of speech produced with a face mask

   https://doi.org/10.1186/s41235-022-00388-4  

   Crinnion, Anne Marie; Toscano, Joseph C.; Toscano, Cheyenne M. (May 2022, Cognitive Research: Principles and Implications)

   Abstract Over the past two years, face masks have been a critical tool for preventing the spread of COVID-19. While previous studies have examined the effects of masks on speech recognition, much of this work was conducted early in the pandemic. Given that human listeners are able to adapt to a wide variety of novel contexts in speech perception, an open question concerns the extent to which listeners have adapted to masked speech during the pandemic. In order to evaluate this, we replicated Toscano and Toscano (PLOS ONE 16(2):e0246842, 2021), looking at the effects of several types of face masks on speech recognition in different levels of multi-talker babble noise. We also examined the effects of listeners’ self-reported frequency of encounters with masked speech and the effects of the implementation of public mask mandates on speech recognition. Overall, we found that listeners’ performance in the current experiment (with data collected in 2021) was similar to that of listeners in Toscano and Toscano (with data collected in 2020) and that performance did not differ based on mask experience. These findings suggest that listeners may have already adapted to masked speech by the time data were collected in 2020, are unable to adapt to masked speech, require additional context to be able to adapt, or that talkers also changed their productions over time. Implications for theories of perceptual learning in speech are discussed. 

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5. Increasing Importance of Joint Analysis of Audio and Video in Computer Vision: A Survey

   https://doi.org/10.1109/ACCESS.2024.3391817  

   Shahabaz, Ahmed; Sarkar, Sudeep (January 2024, IEEE Access)

   The joint analysis of audio and video is a powerful tool that can be applied to various contexts, including action, speech, and sound recognition, audio-visual video parsing, emotion recognition in affective computing, and self-supervised training of deep learning models. Solving these problems often involves tackling core audio-visual tasks, such as audio-visual source localization, audio-visual correspondence, and audio-visual source separation, which can be combined in various ways to achieve the desired results. This paper provides a review of the literature in this area, discussing the advancements, history, and datasets of audio-visual learning methods for various application domains. It also presents an overview of the reported performances on standard datasets and suggests promising directions for future research. 

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