How can technology help analyze emotional expressions in Japanese speech

Technology can analyze emotional expressions in Japanese speech through advanced methods such as emotional speech corpora, speech synthesis, and recognition models that detect and classify emotions using prosody, phonetic features, and sentiment analysis. Recent developments include Japanese emotional speech corpora like JVNV, which incorporate both verbal content and nonverbal vocalizations essential for conveying emotions. Transformer-based models and deep learning architectures such as recurrent neural networks (RNN) and long short-term memory (LSTM) are employed to improve speech emotion recognition accuracy. These models analyze pitch, speech rate, accentuation, and other prosodic features specific to Japanese language to identify emotions such as anger, joy, and sadness reliably. Furthermore, multimodal approaches integrate audio, text, and facial expression data for a more comprehensive emotional understanding. These technologies enable more natural and context-aware human-computer interactions, emotional text-to-speech systems, and emotion-driven 3D facial animations based on Japanese speech. ^{1, 2, 3, 4, 5, 6}

References

• JVNV: A Corpus of Japanese Emotional Speech With Verbal Content and Nonverbal Expressions

• Enhanced Emotional Speech Analysis Using Recurrent Neural Network

• Intelligent Speech Recognition Technology in Japanese using Transformer-based Models and Sentiment Analysis

• On the differences in prosodic features of emotional expressions in Japanese speech according to the degree of the emotion

• Research And Application Analysis of Multimodal Emotion Recognition Methods Based on Speech, Text, And Facial Expressions

• EmotionFace: Speech-Driven Emotional 3D Face Animation Based on Facial Decoupling

• FastSpeech2 Based Japanese Emotional Speech Synthesis

• A prosodic analysis of emotional expressions in Langkat Malay speech

• CSTalk: Correlation Supervised Speech-driven 3D Emotional Facial Animation Generation

• Emotional Text-To-Speech in Japanese Using Artificially Augmented Dataset

• JVNV: A Corpus of Japanese Emotional Speech with Verbal Content and Nonverbal Expressions

• JNV Corpus: A Corpus of Japanese Nonverbal Vocalizations with Diverse Phrases and Emotions

• Algorithmic voice transformations reveal the phonological basis of language-familiarity effects in cross-cultural emotion judgments

• Algorithmic voice transformations reveal the phonological basis of language-familiarity effects in cross-cultural emotion judgments

• How emotion is experienced and expressed in multiple cultures: a large-scale experiment across North America, Europe, and Japan

• Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition

• MelTrans: Mel-Spectrogram Relationship-Learning for Speech Emotion Recognition via Transformers

• Deep Emotion Recognition in Textual Conversations: A Survey

• Emotion Analysis from Voice Signals: A Machine Learning Approach

• A comprehensive study on bilingual and multilingual speech emotion recognition using a two-pass classification scheme

• Textless Speech Emotion Conversion using Discrete and Decomposed Representations

• How emotion is experienced and expressed in multiple cultures: a large-scale experiment across North America, Europe, and Japan

• A Study of Cross-Linguistic Speech Emotion Recognition Based on 2D Feature Spaces

• The Performance of Artificial Intelligence Translation App in Japanese Language Education Guided by Deep Learning

• Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction

• SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection