Audio Sentiment Analysis via Distillation and Cross-Modal Integration of Generated Multilingual Transcripts
arXiv:2607.06611v1 Announce Type: new Abstract: Automatically recognizing the sentiment, positive or negative, from speech is a challenging task, requiring both the analysis of vocal inflections and the interpretation of uttered words. Recent solutions rely on audio foundation models to solve the task, but it remains unclear if such models can take all aspects into account. To this end, we propose a multimodal solution that integrates audio and text information via cross-modal transformers, where text transcripts are automatically generated via an automatic speech recognition (ASR) tool. Moreo
The paper leverages recent advancements in audio foundation models and large language models for ASR to address the complex task of audio sentiment analysis, integrating multimodal insights.
This research highlights progress in AI's ability to interpret nuanced human communication, which has significant implications for human-computer interaction, customer service, and data analysis.
The proposed multimodal approach combines vocal inflections with textual content, offering a more robust and accurate method for understanding sentiment from speech compared to previous unimodal solutions.
- · AI developers
- · Customer service platforms
- · Speech recognition companies
- · Data analytics firms
- · Monolinguistic sentiment analysis tools
- · Basic keyword-based sentiment analysis
Improved accuracy in automated sentiment analysis for various applications.
Enhanced development of AI agents capable of more sophisticated emotional understanding.
Potential for more personalized and empathetic AI interactions across industries.
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Read at arXiv cs.CL