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

   Tao, Fei and (January 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 infor- mation 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 mem- ory of the units is limited to a few frames, since the recur- rent 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 mul- tiple 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. Multimodaltrace: Deepfake Detection using Audiovisual Representation Learning

   Muhammad Anas Raza Khalid Mahmood Malik (June 2023, IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, CA, 2023)

   By employing generative deep learning techniques, Deepfakes are created with the intent to create mistrust in society, manipulate public opinion and political decisions, and for other malicious purposes such as blackmail, scamming, and even cyberstalking. As realistic deepfake may involve manipulation of either audio or video or both, thus it is important to explore the possibility of detecting deepfakes through the inadequacy of generative algorithms to synchronize audio and visual modalities. Prevailing performant methods, either detect audio or video cues for deepfakes detection while few ensemble the results after predictions on both modalities without inspecting relationship between audio and video cues. Deepfake detection using joint audiovisual representation learning is not explored much. Therefore, this paper proposes a unified multimodal framework, Multimodaltrace, which extracts learned channels from audio and visual modalities, mixes them independently in IntrAmodality Mixer Layer (IAML), processes them jointly in IntErModality Mixer Layers (IEML) from where it is fed to multilabel classification head. Empirical results show the effectiveness of the proposed framework giving state-of-the-art accuracy of 92.9% on the FakeAVCeleb dataset. The cross-dataset evaluation of the proposed framework on World Leaders and Presidential Deepfake Detection Datasets gives an accuracy of 83.61% and 70% respectively. The study also provides insights into how the model focuses on different parts of audio and visual features through integrated gradient analysis 

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5. Improving Speech Emotion Recognition Using Self-Supervised Learning with Domain-Specific Audiovisual Tasks

   https://doi.org/10.21437/Interspeech.2022-11012  

   Goncalves, Lucas; Busso, Carlos (September 2022, Interspeech 2022)

   Speech emotion recognition (SER) is a challenging task due to the limited availability of real-world labeled datasets. Since it is easier to find unlabeled data, the use of self-supervised learning (SSL) has become an attractive alternative. This study proposes new pre-text tasks for SSL to improve SER. While our target application is SER, the proposed pre-text tasks include audio-visual formulations, leveraging the relationship between acoustic and facial features. Our proposed approach introduces three new unimodal and multimodal pre-text tasks that are carefully designed to learn better representations for predicting emotional cues from speech. Task 1 predicts energy variations (high or low) from a speech sequence. Task 2 uses speech features to predict facial activation (high or low) based on facial landmark movements. Task 3 performs a multi-class emotion recognition task on emotional labels obtained from combinations of action units (AUs) detected across a video sequence. We pre-train a network with 60.92 hours of unlabeled data, fine-tuning the model for the downstream SER task. The results on the CREMA-D dataset show that the model pre-trained on the proposed domain-specific pre-text tasks significantly improves the precision (up to 5.1%), recall (up to 4.5%), and F1-scores (up to 4.9%) of our SER system. 

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