by jsendak | Jan 12, 2024 | Computer Science
In this paper, we present a multimodal and dynamical VAE (MDVAE) applied to
unsupervised audio-visual speech representation learning. The latent space is
structured to dissociate the latent dynamical factors that are shared between
the modalities from those that are specific to each modality. A static latent
variable is also introduced to encode the information that is constant over
time within an audiovisual speech sequence. The model is trained in an
unsupervised manner on an audiovisual emotional speech dataset, in two stages.
In the first stage, a vector quantized VAE (VQ-VAE) is learned independently
for each modality, without temporal modeling. The second stage consists in
learning the MDVAE model on the intermediate representation of the VQ-VAEs
before quantization. The disentanglement between static versus dynamical and
modality-specific versus modality-common information occurs during this second
training stage. Extensive experiments are conducted to investigate how
audiovisual speech latent factors are encoded in the latent space of MDVAE.
These experiments include manipulating audiovisual speech, audiovisual facial
image denoising, and audiovisual speech emotion recognition. The results show
that MDVAE effectively combines the audio and visual information in its latent
space. They also show that the learned static representation of audiovisual
speech can be used for emotion recognition with few labeled data, and with
better accuracy compared with unimodal baselines and a state-of-the-art
supervised model based on an audiovisual transformer architecture.
In this article, we are introduced to the concept of multimodal and dynamical Variational Autoencoder (MDVAE) applied to unsupervised audio-visual speech representation learning. The key idea behind MDVAE is to structure the latent space in a way that it captures the shared and specific information between the audio and visual modalities, as well as the temporal dynamics within an audiovisual speech sequence.
This research is a testament to the multi-disciplinary nature of multimedia information systems, as it combines techniques from computer vision, machine learning, and speech processing. By integrating both audio and visual modalities, this approach paves the way for more immersive and realistic multimedia experiences.
Animations, artificial reality, augmented reality, and virtual realities are all fields that greatly benefit from advancements in audio-visual processing. By effectively combining audio and visual information in the latent space, MDVAE opens up possibilities for creating more realistic and interactive virtual environments. Imagine a virtual reality game where characters not only look real but also sound realistic when they speak. This level of fidelity can greatly enhance the user’s immersion and overall experience.
Furthermore, this research addresses the challenge of disentangling static versus dynamical and modality-specific versus modality-common information. This is crucial for tasks such as audiovisual facial image denoising and emotion recognition. By learning a static representation of audiovisual speech, the model can effectively filter out noise and extract meaningful features that contribute to emotion recognition. The results demonstrate that MDVAE outperforms unimodal baselines and even a state-of-the-art supervised model based on an audiovisual transformer architecture.
Overall, this research showcases the potential of incorporating multimodal and dynamical approaches in the field of multimedia information systems. By harnessing the power of both audio and visual modalities, we can create more immersive experiences and improve tasks such as animation, artificial reality, augmented reality, and virtual realities. The MDVAE model’s ability to disentangle different factors opens up possibilities for various applications, including emotion recognition and facial image denoising.
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by jsendak | Dec 31, 2023 | AI
Speech emotion recognition (SER) systems aim to recognize human emotional
state during human-computer interaction. Most existing SER systems are trained
based on utterance-level labels. However, not all frames in an audio have
affective states consistent with utterance-level label, which makes it
difficult for the model to distinguish the true emotion of the audio and
perform poorly. To address this problem, we propose a frame-level emotional
state alignment method for SER. First, we fine-tune HuBERT model to obtain a
SER system with task-adaptive pretraining (TAPT) method, and extract embeddings
from its transformer layers to form frame-level pseudo-emotion labels with
clustering. Then, the pseudo labels are used to pretrain HuBERT. Hence, the
each frame output of HuBERT has corresponding emotional information. Finally,
we fine-tune the above pretrained HuBERT for SER by adding an attention layer
on the top of it, which can focus only on those frames that are emotionally
more consistent with utterance-level label. The experimental results performed
on IEMOCAP indicate that our proposed method performs better than
state-of-the-art (SOTA) methods.
SER systems and the importance of frame-level emotional state alignment
Speech emotion recognition (SER) systems play a crucial role in human-computer interaction, as they aim to identify and understand human emotional states. However, most existing SER systems are trained based on utterance-level labels, which poses a challenge when it comes to accurately capturing emotions within individual frames of audio.
This lack of alignment between frame-level emotional states and utterance-level labels can lead to poor performance of SER models, as they struggle to differentiate the true emotion portrayed in the audio. To overcome this limitation, a frame-level emotional state alignment method has been proposed.
The role of task-adaptive pretraining (TAPT)
The proposed method utilizes a task-adaptive pretraining (TAPT) approach to fine-tune the HuBERT model. HuBERT is a transformer-based model that can extract embeddings from its transformer layers. By fine-tuning HuBERT with the TAPT method, frame-level pseudo-emotion labels are generated through clustering techniques.
Clustering allows for the grouping of frames with similar emotional characteristics, providing an approximation of emotional labels at the frame level. The extracted embeddings and pseudo labels are then used for pretraining HuBERT, aligning each frame output of the model with corresponding emotional information.
The importance of attention and fine-tuning
To further enhance the SER system, an attention layer is added on top of the pretrained HuBERT model. This attention layer enables the model to focus its attention on frames that are more emotionally consistent with the utterance-level label. This step helps in improving the overall performance and accuracy of the system.
Finally, the pretrained HuBERT model, along with the attention layer, undergoes fine-tuning specifically for SER. This fine-tuning process ensures that the model is optimized to recognize and classify emotions accurately, leveraging the information captured at both the frame and utterance levels.
The potential applications and multi-disciplinary nature of the proposed method
The proposed frame-level emotional state alignment method has shown promising results when evaluated on the IEMOCAP dataset. By outperforming state-of-the-art methods in SER, this approach opens up new possibilities for real-world applications.
Moreover, the methodology presented in this article highlights the multi-disciplinary nature of SER. It combines techniques from natural language processing (NLP), audio signal processing, and machine learning to achieve accurate emotion recognition. This cross-disciplinary collaboration is essential for developing robust SER systems that can better understand and respond to human emotions, thereby enhancing human-computer interaction experiences across various domains.
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by jsendak | Dec 29, 2023 | Computer Science
Reducing Complexity and Enhancing Robustness in Speech Emotion Recognition
Representations derived from models like BERT and HuBERT have revolutionized speech emotion recognition, achieving remarkable performance. However, these representations come with a high memory and computational cost, as they were not specifically designed for emotion recognition tasks. In this article, we uncover lower-dimensional subspaces within these pre-trained representations that can significantly reduce model complexity without compromising emotion estimation accuracy. Furthermore, we introduce a novel approach to incorporate label uncertainty, in the form of grader opinion variance, into the models, resulting in improved generalization capacity and robustness. Additionally, we conduct experiments to evaluate the robustness of these emotion models against acoustic degradations and find that the reduced-dimensional representations maintain similar performance to their full-dimensional counterparts, making them highly promising for real-world applications.
Abstract:Representations derived from models such as BERT (Bidirectional Encoder Representations from Transformers) and HuBERT (Hidden units BERT), have helped to achieve state-of-the-art performance in dimensional speech emotion recognition. Despite their large dimensionality, and even though these representations are not tailored for emotion recognition tasks, they are frequently used to train large speech emotion models with high memory and computational costs. In this work, we show that there exist lower-dimensional subspaces within the these pre-trained representational spaces that offer a reduction in downstream model complexity without sacrificing performance on emotion estimation. In addition, we model label uncertainty in the form of grader opinion variance, and demonstrate that such information can improve the models generalization capacity and robustness. Finally, we compare the robustness of the emotion models against acoustic degradations and observed that the reduced dimensional representations were able to retain the performance similar to the full-dimensional representations without significant regression in dimensional emotion performance.
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