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1. Audiovisual Speech Activity Detection with Advanced Long Short-Term Memory

   https://doi.org/10.21437/Interspeech.2018-2490  

   Tao, Fei; Busso, Carlos (September 2018, Interspeech 2018)

   Speech activity detection (SAD) is a key pre-processing step for a speech-based system. The performance of conventional audio-only SAD (A-SAD) systems is impaired by acoustic noise when they are used in practical applications. An alternative approach to address this problem is to include visual information, creating audiovisual speech activity detection (AV-SAD) solutions. In our previous work, we proposed to build an AV-SAD system using bimodal recurrent neural network (BRNN). This framework was able to capture the task-related characteristics in the audio and visual inputs, and model the temporal information within and across modalities. The approach relied on long short-term memory (LSTM). Although LSTM can model longer temporal dependencies with the cells, the effective memory of the units is limited to a few frames, since the recurrent connection only considers the previous frame. For SAD systems, it is important to model longer temporal dependencies to capture the semi-periodic nature of speech conveyed in acoustic and orofacial features. This study proposes to implement a BRNN-based AV-SAD system with advanced LSTMs (A-LSTMs), which overcomes this limitation by including multiple connections to frames in the past. The results show that the proposed framework can significantly outperform the BRNN system trained with the original LSTM layers. 

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2. Aligning Audiovisual Features for Audiovisual Speech Recognition

   https://doi.org/10.1109/ICME.2018.8486455  

   Tao, Fei; Busso, Carlos (July 2018, IEEE International Conference on Multimedia and Expo (ICME))

   Speech activity detection (SAD) is a key pre-processing step for a speech-based system. The performance of conventional audio-only SAD (A-SAD) systems is impaired by acoustic noise when they are used in practical applications. An alternative approach to address this problem is to include visual information, creating audiovisual speech activity detection (AV-SAD) solutions. In our previous work, we proposed to build an AV-SAD system using bimodal recurrent neural network (BRNN). This framework was able to capture the task-related characteristics in the audio and visual inputs, and model the temporal information within and across modalities. The approach relied on long short-term memory (LSTM). Although LSTM can model longer temporal dependencies with the cells, the effective memory of the units is limited to a few frames, since the recurrent connection only considers the previous frame. For SAD systems, it is important to model longer temporal dependencies to capture the semi-periodic nature of speech conveyed in acoustic and orofacial features. This study proposes to implement a BRNN-based AV-SAD system with advanced LSTMs (A-LSTMs), which overcomes this limitation by including multiple connections to frames in the past. The results show that the proposed framework can significantly outperform the BRNN system trained with the original LSTM layers. 

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3. DeepComboSAD: Spectro-Temporal Correlation Based Speech Activity Detection for Naturalistic Audio Streams

   https://doi.org/10.1109/LSP.2023.3319229  

   Joglekar, Aditya; Hansen, John H. (January 2023, IEEE Signal Processing Letters)

   Speech activity detection (SAD) serves as a crucial front-end system to several downstream Speech and Language Technology (SLT) tasks such as speaker diarization, speaker identification, and speech recognition. Recent years have seen deep learning (DL)-based SAD systems designed to improve robustness against static background noise and interfering speakers. However, SAD performance can be severely limited for conversations recorded in naturalistic environments due to dynamic acoustic scenarios and previously unseen non-speech artifacts. In this letter, we propose an end-to-end deep learning framework designed to be robust to time-varying noise profiles observed in naturalistic audio. We develop a novel SAD solution for the UTDallas Fearless Steps Apollo corpus based on NASA’s Apollo missions. The proposed system leverages spectro-temporal correlations with a threshold optimization mechanism to adjust to acoustic variabilities across multiple channels and missions. This system is trained and evaluated on the Fearless Steps Challenge (FSC) corpus (a subset of the Apollo corpus). Experimental results indicate a high degree of adaptability to out-of-domain data, achieving a relative Detection Cost Function (DCF) performance improvement of over 50% compared to the previous FSC baselines and state-of-the-art (SOTA) SAD systems. The proposed model also outperforms the most recent DL-based SOTA systems from FSC Phase-4. Ablation analysis is conducted to confirm the efficacy of the proposed spectro-temporal features. 

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4. Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical Settings

   https://doi.org/10.1145/3580798  

   Gao, Chenyang; Marsic, Ivan; Sarcevic, Aleksandra; Gestrich-Thompson, Waverly; Burd, Randall S. (March 2022, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   In clinical settings, most automatic recognition systems use visual or sensory data to recognize activities. These systems cannot recognize activities that rely on verbal assessment, lack visual cues, or do not use medical devices. We examined speech-based activity and activity-stage recognition in a clinical domain, making the following contributions. (1) We collected a high-quality dataset representing common activities and activity stages during actual trauma resuscitation events-the initial evaluation and treatment of critically injured patients. (2) We introduced a novel multimodal network based on audio signal and a set of keywords that does not require a high-performing automatic speech recognition (ASR) engine. (3) We designed novel contextual modules to capture dynamic dependencies in team conversations about activities and stages during a complex workflow. (4) We introduced a data augmentation method, which simulates team communication by combining selected utterances and their audio clips, and showed that this method contributed to performance improvement in our data-limited scenario. In offline experiments, our proposed context-aware multimodal model achieved F1-scores of 73.2±0.8% and 78.1±1.1% for activity and activity-stage recognition, respectively. In online experiments, the performance declined about 10% for both recognition types when using utterance-level segmentation of the ASR output. The performance declined about 15% when we omitted the utterance-level segmentation. Our experiments showed the feasibility of speech-based activity and activity-stage recognition during dynamic clinical events. 

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5. Leveraging Multiple Sources in Automatic African American English Dialect Detection for Adults and Children

   https://doi.org/10.1109/ICASSP49357.2023.10096614  

   Johnson, Alexander; Shetty, Vishwas; Ostendorf, Mari; and Alwan, Abeer (June 2023, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   IEEE SIGNAL PROCESSING SOCIETY (Ed.)

   This paper 1 presents a novel system which utilizes acoustic, phonological, morphosyntactic, and prosodic information for binary automatic dialect detection of African American English. We train this system utilizing adult speech data and then evaluate on both children’s and adults’ speech with unmatched training and testing scenarios. The proposed system combines novel and state-of-the-art architectures, including a multi-source transformer language model pre-trained on Twitter text data and fine-tuned on ASR transcripts as well as an LSTM acoustic model trained on self-supervised learning representations, in order to learn a comprehensive view of dialect. We show robust, explainable performance across recording conditions for different features for adult speech, but fusing multiple features is important for good results on children’s speech. 

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