Improvement and Implementation of a Speech Emotion Recognition Model Based on Dual-Layer LSTM
This paper builds upon an existing speech emotion recognition model by adding an additional LSTM layer to improve the accuracy and processing efficiency of emotion recognition from audio data. By capturing the long-term dependencies within audio sequences through a dual-layer LSTM network, the model can recognize and classify complex emotional patterns more accurately. Experiments conducted on the RAVDESS dataset validated this approach, showing that the modified dual layer LSTM model improves accuracy by 2% compared to the single-layer LSTM while significantly reducing recognition latency, thereby enhancing real-time performance. These results indicate that the dual-layer LSTM architecture is highly suitable for handling emotional features with long-term dependencies, providing a viable optimization for speech emotion recognition systems. This research provides a reference for practical applications in fields like intelligent customer service, sentiment analysis and human-computer interaction.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion RecognitionSentiment AnalysisSpeech Emotion RecognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Prompt Amplification and Zero-Shot Late Fusion in Audio-Language Models for Speech Emotion Recognition
Audio-Language Models (ALMs) are making strides in understanding speech and non-speech audio. However, domain-specialist Foundation Models (FMs) remain the best for closed-ended speech processing tasks such as Speech Emo…
Speech Emotion RecognitionCurriculum Learning for Speech Emotion Recognition from Crowdsourced Labels
This study introduces a method to design a curriculum for machine-learning to maximize the efficiency during the training process of deep neural networks (DNNs) for speech emotion recognition. Previous studies in other m…
Emotion RecognitionMulti-class ClassificationSpeech Emotion RecognitionSpeech Emotion Recognition Using Speech Feature and Word Embedding
—Emotion recognition can be performed automatically from many modalities. This paper presents a categorical speech emotion recognition using speech features and word embedding. Text features can be combined with speech f…
Emotion RecognitionSpeech Emotion RecognitionMulti-stream Attention-based BLSTM with Feature Segmentation for Speech Emotion Recognition
This paper proposes a speech emotion recognition technique that considers the suprasegmental characteristics and temporal change of individual speech parameters. In recent years, speech emotion recognition using Bidir…
Data AugmentationEmotional Speech SynthesisEmotion RecognitionSpeech Emotion Recognition+1EmoAra: Emotion-Preserving English Speech Transcription and Cross-Lingual Translation with Arabic Text-to-Speech
This work presents EmoAra, an end-to-end emotion-preserving pipeline for cross-lingual spoken communication, motivated by banking customer service where emotional context affects service quality. EmoAra integrates Speech…
Speech Emotion RecognitionEmotion ClassificationMachine TranslationSpeech Recognition