Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation

• URL: http://arxiv.org/abs/2109.02915v1
• Date: Tue, 7 Sep 2021 08:04:02 GMT
• Title: Few-shot Learning in Emotion Recognition of Spontaneous Speech Using a Siamese Neural Network with Adaptive Sample Pair Formation
• Authors: Kexin Feng and Theodora Chaspari
• Abstract summary: This paper proposes a few-shot learning approach for automatically recognizing emotion in spontaneous speech from a small number of labelled samples. Few-shot learning is implemented via a metric learning approach through a siamese neural network. Results indicate the feasibility of the proposed metric learning in recognizing emotions from spontaneous speech in four datasets.
• Score: 11.592365534228895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech-based machine learning (ML) has been heralded as a promising solution for tracking prosodic and spectrotemporal patterns in real-life that are indicative of emotional changes, providing a valuable window into one's cognitive and mental state. Yet, the scarcity of labelled data in ambulatory studies prevents the reliable training of ML models, which usually rely on "data-hungry" distribution-based learning. Leveraging the abundance of labelled speech data from acted emotions, this paper proposes a few-shot learning approach for automatically recognizing emotion in spontaneous speech from a small number of labelled samples. Few-shot learning is implemented via a metric learning approach through a siamese neural network, which models the relative distance between samples rather than relying on learning absolute patterns of the corresponding distributions of each emotion. Results indicate the feasibility of the proposed metric learning in recognizing emotions from spontaneous speech in four datasets, even with a small amount of labelled samples. They further demonstrate superior performance of the proposed metric learning compared to commonly used adaptation methods, including network fine-tuning and adversarial learning. Findings from this work provide a foundation for the ambulatory tracking of human emotion in spontaneous speech contributing to the real-life assessment of mental health degradation.

Related papers

• Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences [4.740624855896404]
  We propose a contrastive learning framework utilizing selective strong augmentation for self-supervised gait-based emotion representation. Our approach is validated on the Emotion-Gait (E-Gait) and Emilya datasets and outperforms the state-of-the-art methods under different evaluation protocols.
  arXiv  Detail & Related papers  (2024-05-08T09:13:10Z)
• Self-Supervised Learning for Audio-Based Emotion Recognition [1.7598252755538808]
  Self-supervised learning is a family of methods which can learn despite a scarcity of supervised labels. We have applied self-supervised learning pre-training to the classification of emotions from the CMU- MOSEI's acoustic modality. We find that self-supervised learning consistently improves the performance of the model across all metrics.
  arXiv  Detail & Related papers  (2023-07-23T14:40:50Z)
• Phonetic and Prosody-aware Self-supervised Learning Approach for Non-native Fluency Scoring [13.817385516193445]
  Speech fluency/disfluency can be evaluated by analyzing a range of phonetic and prosodic features. Deep neural networks are commonly trained to map fluency-related features into the human scores. We introduce a self-supervised learning (SSL) approach that takes into account phonetic and prosody awareness for fluency scoring.
  arXiv  Detail & Related papers  (2023-05-19T05:39:41Z)
• Measures of Information Reflect Memorization Patterns [53.71420125627608]
  We show that the diversity in the activation patterns of different neurons is reflective of model generalization and memorization. Importantly, we discover that information organization points to the two forms of memorization, even for neural activations computed on unlabelled in-distribution examples.
  arXiv  Detail & Related papers  (2022-10-17T20:15:24Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Affect-DML: Context-Aware One-Shot Recognition of Human Affect using Deep Metric Learning [29.262204241732565]
  Existing methods assume that all emotions-of-interest are given a priori as annotated training examples. We conceptualize one-shot recognition of emotions in context -- a new problem aimed at recognizing human affect states in finer particle level from a single support sample. All variants of our model clearly outperform the random baseline, while leveraging the semantic scene context consistently improves the learnt representations.
  arXiv  Detail & Related papers  (2021-11-30T10:35:20Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• Preliminary study on using vector quantization latent spaces for TTS/VC systems with consistent performance [55.10864476206503]
  We investigate the use of quantized vectors to model the latent linguistic embedding. By enforcing different policies over the latent spaces in the training, we are able to obtain a latent linguistic embedding. Our experiments show that the voice cloning system built with vector quantization has only a small degradation in terms of perceptive evaluations.
  arXiv  Detail & Related papers  (2021-06-25T07:51:35Z)
• Empowering Language Understanding with Counterfactual Reasoning [141.48592718583245]
  We propose a Counterfactual Reasoning Model, which mimics the counterfactual thinking by learning from few counterfactual samples. In particular, we devise a generation module to generate representative counterfactual samples for each factual sample, and a retrospective module to retrospect the model prediction by comparing the counterfactual and factual samples.
  arXiv  Detail & Related papers  (2021-06-06T06:36:52Z)
• A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition [0.0]
  We show a transfer learning method in speech emotion recognition based on a Time-Delay Neural Network architecture. We achieve the highest significantly higher accuracy when compared to state-of-the-art, using five-fold cross validation.
  arXiv  Detail & Related papers  (2020-08-06T20:37:22Z)
• Automatic Recall Machines: Internal Replay, Continual Learning and the Brain [104.38824285741248]
  Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity. We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective. Instead the implicit memory of learned samples within the assessed model itself is exploited.
  arXiv  Detail & Related papers  (2020-06-22T15:07:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.