Ensemble-Within-Ensemble Classification for Escalation Prediction from Speech

Verkholyak, Oxana; Dresvyanskiy, Denis; Dvoynikova, Anastasia; Kotov, Denis; Ryumina, Elena; Velichko, Alena; Mamontov, Danila; Minker, Wolfgang; Karpov, Alexey

doi:10.21437/interspeech.2021-1821

Cited by 2 publications

(2 citation statements)

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“…Log-Mel spectrograms are widely used in deep learning for various CV tasks such as speech escalation detection [ 114 ], audio classification [ 115 ], and ASR [ 116 ]. In the current work, we also use log-Mel spectrograms, and as deep learning models we implement three 2DCNN models: ResNet [ 51 ], VGG [ 117 ], and PANN [ 118 ].…”

Section: Methodsmentioning

confidence: 99%

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Audio-Visual Speech and Gesture Recognition by Sensors of Mobile Devices

Ryumin

Ivanko

Ryumina

2023

Sensors

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Audio-visual speech recognition (AVSR) is one of the most promising solutions for reliable speech recognition, particularly when audio is corrupted by noise. Additional visual information can be used for both automatic lip-reading and gesture recognition. Hand gestures are a form of non-verbal communication and can be used as a very important part of modern human–computer interaction systems. Currently, audio and video modalities are easily accessible by sensors of mobile devices. However, there is no out-of-the-box solution for automatic audio-visual speech and gesture recognition. This study introduces two deep neural network-based model architectures: one for AVSR and one for gesture recognition. The main novelty regarding audio-visual speech recognition lies in fine-tuning strategies for both visual and acoustic features and in the proposed end-to-end model, which considers three modality fusion approaches: prediction-level, feature-level, and model-level. The main novelty in gesture recognition lies in a unique set of spatio-temporal features, including those that consider lip articulation information. As there are no available datasets for the combined task, we evaluated our methods on two different large-scale corpora—LRW and AUTSL—and outperformed existing methods on both audio-visual speech recognition and gesture recognition tasks. We achieved AVSR accuracy for the LRW dataset equal to 98.76% and gesture recognition rate for the AUTSL dataset equal to 98.56%. The results obtained demonstrate not only the high performance of the proposed methodology, but also the fundamental possibility of recognizing audio-visual speech and gestures by sensors of mobile devices.

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Section: Methodsmentioning

confidence: 99%

“…In the current work, we also use log-Mel spectrograms, and as deep learning models we implement three 2DCNN models: ResNet [ 51 ], VGG [ 117 ], and PANN [ 118 ]. The selected models have been repeatedly used in CV tasks for audio modality processing [ 114 , 116 ]. Model architectures are shown in Figure 4 .…”

Section: Methodsmentioning

confidence: 99%