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Register a new userMulti-Modal Emotion Detection with Transfer Learning
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Automated emotion detection in speech is a challenging task due to the complex interdependence between words and the manner in which they are spoken. It is made more difficult by the available datasets; their small size and incompatible labeling idiosyncrasies make it hard to build generalizable emotion detection systems. To address these two challenges, we present a multi-modal approach that first transfers learning from related tasks in speech and text to produce robust neural embeddings and then uses these embeddings to train a pLDA classifier that is able to adapt to previously unseen emotions and domains. We begin by training a multilayer TDNN on the task of speaker identification with the VoxCeleb corpora and then fine-tune it on the task of emotion identification with the Crema-D corpus. Using this network, we extract speech embeddings for Crema-D from each of its layers, generate and concatenate text embeddings for the accompanying transcripts using a fine-tuned BERT model and then train an LDA - pLDA classifier on the resulting dense representations. We exhaustively evaluate the predictive power of every component: the TDNN alone, speech embeddings from each of its layers alone, text embeddings alone and every combination thereof. Our best variant, trained on only VoxCeleb and Crema-D and evaluated on IEMOCAP, achieves an EER of 38.05%. Including a portion of IEMOCAP during training produces a 5-fold averaged EER of 25.72% (For comparison, 44.71% of the gold-label annotations include at least one annotator who disagrees).
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Emotion represents an essential aspect of human speech that is manifested in speech prosody. Speech, visual, and textual cues are complementary in human communication. In this paper, we study a hybrid fusion method, referred to as multi-modal attention network (MMAN) to make use of visual and textual cues in speech emotion recognition. We propose a novel multi-modal attention mechanism, cLSTM-MMA, which facilitates the attention across three modalities and selectively fuse the information. cLSTM-MMA is fused with other uni-modal sub-networks in the late fusion. The experiments show that speech emotion recognition benefits significantly from visual and textual cues, and the proposed cLSTM-MMA alone is as competitive as other fusion methods in terms of accuracy, but with a much more compact network structure. The proposed hybrid network MMAN achieves state-of-the-art performance on IEMOCAP database for emotion recognition.
General embeddings like word2vec, GloVe and ELMo have shown a lot of success in natural language tasks. The embeddings are typically extracted from models that are built on general tasks such as skip-gram models and natural language generation. In this paper, we extend the work from natural language understanding to multi-modal architectures that use audio, visual and textual information for machine learning tasks. The embeddings in our network are extracted using the encoder of a transformer model trained using multi-task training. We use person identification and automatic speech recognition as the tasks in our embedding generation framework. We tune and evaluate the embeddings on the downstream task of emotion recognition and demonstrate that on the CMU-MOSEI dataset, the embeddings can be used to improve over previous state of the art results.
Self-supervised learning has attracted plenty of recent research interest. However, most works for self-supervision in speech are typically unimodal and there has been limited work that studies the interaction between audio and visual modalities for cross-modal self-supervision. This work (1) investigates visual self-supervision via face reconstruction to guide the learning of audio representations; (2) proposes an audio-only self-supervision approach for speech representation learning; (3) shows that a multi-task combination of the proposed visual and audio self-supervision is beneficial for learning richer features that are more robust in noisy conditions; (4) shows that self-supervised pretraining can outperform fully supervised training and is especially useful to prevent overfitting on smaller sized datasets. We evaluate our learned audio representations for discrete emotion recognition, continuous affect recognition and automatic speech recognition. We outperform existing self-supervised methods for all tested downstream tasks. Our results demonstrate the potential of visual self-supervision for audio feature learning and suggest that joint visual and audio self-supervision leads to more informative audio representations for speech and emotion recognition.
The research on human emotion under multimedia stimulation based on physiological signals is an emerging field, and important progress has been achieved for emotion recognition based on multi-modal signals. However, it is challenging to make full use of the complementarity among spatial-spectral-temporal domain features for emotion recognition, as well as model the heterogeneity and correlation among multi-modal signals. In this paper, we propose a novel two-stream heterogeneous graph recurrent neural network, named HetEmotionNet, fusing multi-modal physiological signals for emotion recognition. Specifically, HetEmotionNet consists of the spatial-temporal stream and the spatial-spectral stream, which can fuse spatial-spectral-temporal domain features in a unified framework. Each stream is composed of the graph transformer network for modeling the heterogeneity, the graph convolutional network for modeling the correlation, and the gated recurrent unit for capturing the temporal domain or spectral domain dependency. Extensive experiments on two real-world datasets demonstrate that our proposed model achieves better performance than state-of-the-art baselines.
Tacotron-based end-to-end speech synthesis has shown remarkable voice quality. However, the rendering of prosody in the synthesized speech remains to be improved, especially for long sentences, where prosodic phrasing errors can occur frequently. In this paper, we extend the Tacotron-based speech synthesis framework to explicitly model the prosodic phrase breaks. We propose a multi-task learning scheme for Tacotron training, that optimizes the system to predict both Mel spectrum and phrase breaks. To our best knowledge, this is the first implementation of multi-task learning for Tacotron based TTS with a prosodic phrasing model. Experiments show that our proposed training scheme consistently improves the voice quality for both Chinese and Mongolian systems.
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