Summary of Cascaded Cross-modal Transformer For Audio-textual Classification, by Nicolae-catalin Ristea et al.
Cascaded Cross-Modal Transformer for Audio-Textual Classification
by Nicolae-Catalin Ristea, Andrei Anghel, Radu Tudor Ionescu
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach in this paper tackles the challenge of speech classification tasks with limited training data by leveraging multimodal representations. The method involves transcribing speech using automatic speech recognition (ASR) models, translating transcripts into different languages via pretrained translation models, and combining language-specific BERT features with Wav2Vec2.0 audio features through a novel cascaded cross-modal transformer (CCMT). This approach is demonstrated to achieve superior classification performance on the Requests Sub-Challenge of the ACM Multimedia 2023 Computational Paralinguistics Challenge, as well as outperforming previous studies on the Speech Commands v2 and HarperValleyBank dialog data sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make computers better at understanding spoken language. The idea is to use special tools that can recognize what’s being said, translate it into different languages, and then mix all that information together to get a better sense of what the speech means. This helps when there isn’t much data available for training, which is often the case with new languages or accents. The approach works really well, beating previous methods on some important tests. |
Keywords
* Artificial intelligence * Bert * Classification * Transformer * Translation
