Emotion Beyond Words: Deep Learning Approaches to Detect Sentiment and Tone in Multilingual Speech

Abstract

Speech carries sentiment and emotion in its words and also in pitch, loudness, rhythm, and voice quality, and these cues are shaped by language and culture. Systems that detect sentiment and tone from speech must therefore work across languages, speakers, and recording conditions, yet most were built and tested on a few acted English or German corpora. This paper reviews the empirical evidence on deep-learning approaches to multilingual speech emotion and sentiment recognition. It combines emotion theory and cross-cultural perception studies with work on acoustic features, end-to-end networks, self-supervised multilingual encoders, benchmark corpora, multimodal fusion, fairness, and regulation. Four findings emerge. First, self-supervised encoders have changed the field. Multilingual encoders are pretrained on up to 436,000 hours of speech in 128 languages, whereas the standard benchmark corpus, IEMOCAP, holds about 12 hours from ten actors, and fine-tuned transformers have narrowed the long-standing gap in valence prediction. Second, people recognize vocal emotion across cultures at above-chance rates but with an in-group advantage, and machine systems show a parallel pattern, with accuracy falling under cross-corpus and cross-lingual testing. Third, evaluation practice limits what can be concluded: on a 300-utterance test set, 70 percent accuracy carries a 95 percent interval of about ±5 points, wider than many reported gains. Fourth, the risks are concrete. Commercial speech recognizers had an average word error rate of 0.35 for Black speakers against 0.19 for white speakers, and the EU AI Act prohibits emotion inference in workplaces and educational institutions, except for medical or safety reasons. Two comparative tables map methods to evidence and challenges to mitigations. The paper then proposes a calibrated multilingual pipeline with an evaluation protocol. The pipeline is a conceptual synthesis of the reviewed literature and has not been tested experimentally.