Speech Emotion Recognition Using Machine Learning
PulseAugur coverage of Speech Emotion Recognition Using Machine Learning — every cluster mentioning Speech Emotion Recognition Using Machine Learning across labs, papers, and developer communities, ranked by signal.
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New framework enables single speech encoder to learn multiple attributes
Researchers have developed a novel multi-attribute multi-teacher distillation framework designed to enable a single self-supervised speech encoder to simultaneously learn multiple speech attributes. This approach utiliz…
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Research explores CNNs for speech emotion recognition in digital healthcare
A research paper submitted to arXiv explored the use of Convolutional Neural Networks (CNNs) for Speech Emotion Recognition (SER). The study aimed to identify human emotions from voice tone and pitch, with a focus on ap…
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Concept bottleneck models enhance explainability in speech emotion recognition
Researchers have adapted concept bottleneck models, previously used for image classification, to speech emotion recognition (SER). This adaptation aims to improve the explainability of SER systems, particularly those ut…
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New framework enhances speech anonymization while preserving data utility
Researchers have developed a new two-stage framework for speech anonymization that aims to preserve both linguistic content and acoustic identity while maintaining data utility. This framework uses a generative speech e…
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New TTS-generated backdoor attacks exploit Speech Emotion Recognition systems
Researchers have identified a new method for backdoor attacks on Speech Emotion Recognition (SER) systems, utilizing text-to-speech (TTS) generated audio. This technique embeds subtle acoustic triggers into speech, whic…
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Whisper model adapted for Persian Speech Emotion Recognition with PCA
Researchers have explored methods to improve Speech Emotion Recognition (SER) for low-resource languages like Persian, focusing on the Whisper model. Their study proposes using Principal Component Analysis (PCA) to redu…
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HyPASE framework uses hyperbolic geometry for efficient LALM fine-tuning
Researchers have developed HyPASE, a novel framework that utilizes hyperbolic geometry for parameter-efficient fine-tuning of Large Audio-Language Models (LALMs) for Speech Emotion Recognition (SER). Unlike traditional …
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New AMRD method enables lightweight speech emotion recognition models
Researchers have developed Adaptive Multi-teacher Relational Distillation (AMRD), a novel method to create lightweight speech emotion recognition (SER) models suitable for on-device applications. AMRD addresses challeng…
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New deep learning models enhance speech emotion recognition accuracy and explainability
Researchers have developed new deep learning techniques for speech emotion recognition (SER), a field crucial for advancing human-computer interaction. One study introduces a hybrid DCRF-BiLSTM model that achieves high …
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New SpeechEQ benchmark evaluates AI emotional intelligence in voice models
Researchers have introduced SpeechEQ, a new framework designed to evaluate the emotional intelligence of speech-language models (SLMs). This framework includes a dataset of 2,265 dialogues and a multi-turn evaluation pr…
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New adapter adds test-time memory to audio LLMs for better emotion recognition
Researchers have developed a novel method called Titans-as-a-Layer (MAL) to enhance conversational speech emotion recognition. This plug-and-play adapter integrates test-time neural memory into large audio language mode…
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Audio language models improve speech emotion recognition with acoustic cues
Researchers have developed a method to improve speech emotion recognition in audio language models by incorporating explicit acoustic cues. By deriving six interpretable acoustic concept tokens from paralinguistic featu…