Bert4CMR: Cross-Market Recommendation with Bidirectional Encoder Representations from Transformer

Real-world multinational e-commerce companies, such as Amazon and eBay, serve in multiple countries and regions. Obviously, these markets have similar goods but different users. Some markets are data-scarce, while others are data-rich. In recent years, cross-market recommendation (CMR) has been proposed to enhance data-scarce markets by leveraging auxiliary information from data-rich markets. Previous works fine-tune the pre-trained model on the local market after freezing part of the parameters or introducing inter-market similarity into the local market to improve the performance of CMR. However, they generally do not consider eliminating the mutual interference between markets. Therefore, the existing methods are neither unable to learn unbiased general knowledge nor efficient transfer reusable information across markets. In this paper, we propose a novel attention-based model called Bert4CMR to simultaneously improve all markets' recommendation performance. Specifically, we employ the attention mechanism to capture user interests by modelling user behavioural sequences. We pre-train the proposed model on global data to learn the general knowledge of items. Then we fine-tune specific target markets to perform local recommendations. We propose market embedding to model the bias of each market and reduce the mutual inference between the parallel markets. Extensive experiments conducted on seven markets show that our model is state-of-the-art. Our model outperforms the suboptimal model by 4.82%, 4.73%, 7.66% and 6.49% on average of seven datasets in terms of four metrics, respectively. We conduct ablation experiments to analyse the effectiveness of the proposed components. Experimental results indicate that our model is able to learn general knowledge through global data and shield the mutual interference between markets.

Disentangling Prosody Representations with Unsupervised Speech Reconstruction

Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic content and speaker identity in Automatic Speech Recognition (ASR) and speaker verification tasks respectively. However, it is still an open challenging research question to extract prosodic information because of the intrinsic association of different attributes, such as timbre and rhythm, and because of the need for unsupervised training schemes to achieve robust large-scale and speaker-independent ASR. The aim of this paper is to address the disentanglement of emotional prosody from speech based on unsupervised reconstruction. Specifically, we identify, design, implement and integrate three crucial components in our proposed speech reconstruction model Prosody2Vec: (1) a unit encoder that transforms speech signals into discrete units for semantic content, (2) a pretrained speaker verification model to generate speaker identity embeddings, and (3) a trainable prosody encoder to learn prosody representations. We first pretrain the Prosody2Vec representations on unlabelled emotional speech corpora, then fine-tune the model on specific datasets to perform Speech Emotion Recognition (SER) and Emotional Voice Conversion (EVC) tasks. Both objective and subjective evaluations on the EVC task suggest that Prosody2Vec effectively captures general prosodic features that can be smoothly transferred to other emotional speech. In addition, our SER experiments on the IEMOCAP dataset reveal that the prosody features learned by Prosody2Vec are complementary and beneficial for the performance of widely used speech pretraining models and surpass the state-of-the-art methods when combining Prosody2Vec with HuBERT representations. Some audio samples can be found on our demo website.

Data Augmentation with Unsupervised Speaking Style Transfer for Speech Emotion Recognition

Currently, the performance of Speech Emotion Recognition (SER) systems is mainly constrained by the absence of large-scale labelled corpora. Data augmentation is regarded as a promising approach, which borrows methods from Automatic Speech Recognition (ASR), for instance, perturbation on speed and pitch, or generating emotional speech utilizing generative adversarial networks. In this paper, we propose EmoAug, a novel style transfer model to augment emotion expressions, in which a semantic encoder and a paralinguistic encoder represent verbal and non-verbal information respectively. Additionally, a decoder reconstructs speech signals by conditioning on the aforementioned two information flows in an unsupervised fashion. Once training is completed, EmoAug enriches expressions of emotional speech in different prosodic attributes, such as stress, rhythm and intensity, by feeding different styles into the paralinguistic encoder. In addition, we can also generate similar numbers of samples for each class to tackle the data imbalance issue. Experimental results on the IEMOCAP dataset demonstrate that EmoAug can successfully transfer different speaking styles while retaining the speaker identity and semantic content. Furthermore, we train a SER model with data augmented by EmoAug and show that it not only surpasses the state-of-the-art supervised and self-supervised methods but also overcomes overfitting problems caused by data imbalance. Some audio samples can be found on our demo website.

Two-level Attention with Two-stage Multi-task Learning for Facial Emotion Recognition

Compared with facial emotion recognition on categorical model, the dimensional emotion recognition can describe numerous emotions of the real world more accurately. Most prior works of dimensional emotion estimation only considered laboratory data and used video, speech or other multi-modal features. The effect of these methods applied on static images in the real world is unknown. In this paper, a two-level attention with two-stage multi-task learning (2Att-2Mt) framework is proposed for facial emotion estimation on only static images. Firstly, the features of corresponding region(position-level features) are extracted and enhanced automatically by first-level attention mechanism. In the following, we utilize Bi-directional Recurrent Neural Network(Bi-RNN) with self-attention(second-level attention) to make full use of the relationship features of different layers(layer-level features) adaptively. Owing to the inherent complexity of dimensional emotion recognition, we propose a two-stage multi-task learning structure to exploited categorical representations to ameliorate the dimensional representations and estimate valence and arousal simultaneously in view of the correlation of the two targets. The quantitative results conducted on AffectNet dataset show significant advancement on Concordance Correlation Coefficient(CCC) and Root Mean Square Error(RMSE), illustrating the superiority of the proposed framework. Besides, extensive comparative experiments have also fully demonstrated the effectiveness of different components.