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Neural network models for audio tasks, such as automatic speech recognition (ASR) and acoustic scene classification (ASC), are susceptible to noise contamination for real-life applications. To improve audio quality, an enhancement module, which can b
Externí odkaz:
http://arxiv.org/abs/2408.06264
Publikováno v:
Proc. INTERSPEECH 2023, 2683-2687
Abusive content in online social networks is a well-known problem that can cause serious psychological harm and incite hatred. The ability to upload audio data increases the importance of developing methods to detect abusive content in speech recordi
Externí odkaz:
http://arxiv.org/abs/2407.20808
Autor:
Triantafyllopoulos, Andreas, Tsangko, Iosif, Gebhard, Alexander, Mesaros, Annamaria, Virtanen, Tuomas, Schuller, Björn
Foundation models (FMs) are increasingly spearheading recent advances on a variety of tasks that fall under the purview of computer audition -- the use of machines to understand sounds. They feature several advantages over traditional pipelines: amon
Externí odkaz:
http://arxiv.org/abs/2407.15672
Emotion and Intent Joint Understanding in Multimodal Conversation (MC-EIU) aims to decode the semantic information manifested in a multimodal conversational history, while inferring the emotions and intents simultaneously for the current utterance. M
Externí odkaz:
http://arxiv.org/abs/2407.02751
Uncertainty Quantification (UQ) is an important building block for the reliable use of neural networks in real-world scenarios, as it can be a useful tool in identifying faulty predictions. Speech emotion recognition (SER) models can suffer from part
Externí odkaz:
http://arxiv.org/abs/2407.01143
Autor:
Gerczuk, Maurice, Amiriparian, Shahin, Lutz, Justina, Strube, Wolfgang, Papazova, Irina, Hasan, Alkomiet, Schuller, Björn W.
In emergency medicine, timely intervention for patients at risk of suicide is often hindered by delayed access to specialised psychiatric care. To bridge this gap, we introduce a speech-based approach for automatic suicide risk assessment. Our study
Externí odkaz:
http://arxiv.org/abs/2407.11012
Autor:
Christ, Lukas, Amiriparian, Shahin, Hawighorst, Friederike, Schill, Ann-Kathrin, Boutalikakis, Angelo, Graf-Vlachy, Lorenz, König, Andreas, Schuller, Björn W.
Flattery is an important aspect of human communication that facilitates social bonding, shapes perceptions, and influences behavior through strategic compliments and praise, leveraging the power of speech to build rapport effectively. Its automatic d
Externí odkaz:
http://arxiv.org/abs/2406.17667
Autor:
Chang, Yi, Ren, Zhao, Zhao, Zhonghao, Nguyen, Thanh Tam, Qian, Kun, Schultz, Tanja, Schuller, Björn W.
Speech emotion recognition (SER) plays a crucial role in human-computer interaction. The emergence of edge devices in the Internet of Things (IoT) presents challenges in constructing intricate deep learning models due to constraints in memory and com
Externí odkaz:
http://arxiv.org/abs/2406.15119
Autor:
Chen, Tian-Yue, Ren, Haowen, Ghazikhanian, Nareg, Hage, Ralph El, Sasaki, Dayne Y., Salev, Pavel, Takamura, Yayoi, Schuller, Ivan K., Kent, Andrew D.
Metal-insulator transitions (MITs) in resistive switching materials can be triggered by an electric stimulus that produces significant changes in the electrical response. When these phases have distinct magnetic characteristics, dramatic changes in s
Externí odkaz:
http://arxiv.org/abs/2406.11679
Foundation models have shown great promise in speech emotion recognition (SER) by leveraging their pre-trained representations to capture emotion patterns in speech signals. To further enhance SER performance across various languages and domains, we
Externí odkaz:
http://arxiv.org/abs/2406.10275