Improving speaker turn embedding by crossmodal transfer learning from face embedding

Learning speaker turn embeddings has shown considerable improvement in situations where conventional speaker modeling approaches fail. However, this improvement is relatively limited when compared to the gain observed in face embedding learning, which has been proven very successful for face verification and clustering tasks. Assuming that face and voices from the same identities share some latent properties (like age, gender, ethnicity), we propose three transfer learning approaches to leverage the knowledge from the face domain (learned from thousands of images and identities) for tasks in the speaker domain. These approaches, namely target embedding transfer, relative distance transfer, and clustering structure transfer, utilize the structure of the source face embedding space at different granularities to regularize the target speaker turn embedding space as optimizing terms. Our methods are evaluated on two public broadcast corpora and yield promising advances over competitive baselines in verification and audio clustering tasks, especially when dealing with short speaker utterances. The analysis of the results also gives insight into characteristics of the embedding spaces and shows their potential applications

Learning Multimodal Temporal Representation for Dubbing Detection in Broadcast Media

Person discovery in the absence of prior identity knowledge requires accurate association of visual and auditory cues. In broadcast data, multimodal analysis faces additional challenges due to narrated voices over muted scenes or dubbing in different languages. To address these challenges, we define and analyze the problem of dubbing detection in broadcast data, which has not been explored before. We propose a method to represent the temporal relationship between the auditory and visual streams. This method consists of canonical correlation analysis to learn a joint multimodal space, and long short term memory (LSTM) networks to model cross-modality temporal dependencies. Our contributions also include the introduction of a newly acquired dataset of face-speech segments from TV data, which we have made publicly available. The proposed method achieves promising performance on this real world dataset as compared to several baselines

A Domain Adaptation Approach to Improve Speaker Turn Embedding Using Face Representation

This paper proposes a novel approach to improve speaker modeling using knowledge transferred from face representation. In particular, we are interested in learning a discriminative metric which allows speaker turns to be compared directly, which is beneficial for tasks such as diarization and dialogue analysis. Our method improves the embedding space of speaker turns by applying maximum mean discrepancy loss to minimize the disparity between the distributions of facial and acoustic embedded features. This approach aims to discover the shared underlying structure of the two embedded spaces, thus enabling the transfer of knowledge from the richer face representation to the counterpart in speech. Experiments are conducted on broadcast TV news datasets, REPERE and ETAPE, to demonstrate the validity of our method. Quantitative results in verification and clustering tasks show promising improvement, especially in cases where speaker turns are short or the training data size is limited

Speaker Diarization Utilizando LSTM e Spectral Clustering

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia Eletrônica.Este trabalho apresenta um sistema de Speaker Diarization. Em aplicações envolvendo este sistema, deve-se marcar em um canal de áudio os trechos de fala correspondentes a determinados locutores. Neste trabalho são apresentadas uma breve revisão do estado da arte, uma implementação do método considerado o estado da arte em Speaker Diarization, como também uma avaliação de desempenho do sistema implementado

A conditional random field approach for audio-visual people diarization

International audienceWe investigate the problem of audiovisual (AV) person di-arization in broadcast data. That is, automatically associate the faces and voices of people and determine when they appear or speak in the video. The contributions are twofolds. First, we formulate the problem within a novel CRF framework that simultaneously performs the AV association of voices and face clusters to build AV person models, and the joint segmentation of the audio and visual streams using a set of AV cues and their association strength. Secondly, we use for this AV association strength a score that does not only rely on lips activity, but also on contextual visual information (face size, position, number of detected faces,.. .) that leads to more reliable association measures. Experiments on 6 hours of broadcast data show that our framework is able to improve the AV-person diarization especially for speaker segments erroneously labeled in the mono-modal case

Self-supervised Face Representation Learning

This thesis investigates fine-tuning deep face features in a self-supervised manner for discriminative face representation learning, wherein we develop methods to automatically generate pseudo-labels for training a neural network. Most importantly solving this problem helps us to advance the state-of-the-art in representation learning and can be beneficial to a variety of practical downstream tasks. Fortunately, there is a vast amount of videos on the internet that can be used by machines to learn an effective representation. We present methods that can learn a strong face representation from large-scale data be the form of images or video. However, while learning a good representation using a deep learning algorithm requires a large-scale dataset with manually curated labels, we propose self-supervised approaches to generate pseudo-labels utilizing the temporal structure of the video data and similarity constraints to get supervision from the data itself. We aim to learn a representation that exhibits small distances between samples from the same person, and large inter-person distances in feature space. Using metric learning one could achieve that as it is comprised of a pull-term, pulling data points from the same class closer, and a push-term, pushing data points from a different class further away. Metric learning for improving feature quality is useful but requires some form of external supervision to provide labels for the same or different pairs. In the case of face clustering in TV series, we may obtain this supervision from tracks and other cues. The tracking acts as a form of high precision clustering (grouping detections within a shot) and is used to automatically generate positive and negative pairs of face images. Inspired from that we propose two variants of discriminative approaches: Track-supervised Siamese network (TSiam) and Self-supervised Siamese network (SSiam). In TSiam, we utilize the tracking supervision to obtain the pair, additional we include negative training pairs for singleton tracks -- tracks that are not temporally co-occurring. As supervision from tracking may not always be available, to enable the use of metric learning without any supervision we propose an effective approach SSiam that can generate the required pairs automatically during training. In SSiam, we leverage dynamic generation of positive and negative pairs based on sorting distances (i.e. ranking) on a subset of frames and do not have to only rely on video/track based supervision. Next, we present a method namely Clustering-based Contrastive Learning (CCL), a new clustering-based representation learning approach that utilizes automatically discovered partitions obtained from a clustering algorithm (FINCH) as weak supervision along with inherent video constraints to learn discriminative face features. As annotating datasets is costly and difficult, using label-free and weak supervision obtained from a clustering algorithm as a proxy learning task is promising. Through our analysis, we show that creating positive and negative training pairs using clustering predictions help to improve the performance for video face clustering. We then propose a method face grouping on graphs (FGG), a method for unsupervised fine-tuning of deep face feature representations. We utilize a graph structure with positive and negative edges over a set of face-tracks based on their temporal structure of the video data and similarity-based constraints. Using graph neural networks, the features communicate over the edges allowing each track\u27s feature to exchange information with its neighbors, and thus push each representation in a direction in feature space that groups all representations of the same person together and separates representations of a different person. Having developed these methods to generate weak-labels for face representation learning, next we propose to learn compact yet effective representation for describing face tracks in videos into compact descriptors, that can complement previous methods towards learning a more powerful face representation. Specifically, we propose Temporal Compact Bilinear Pooling (TCBP) to encode the temporal segments in videos into a compact descriptor. TCBP possesses the ability to capture interactions between each element of the feature representation with one-another over a long-range temporal context. We integrated our previous methods TSiam, SSiam and CCL with TCBP and demonstrated that TCBP has excellent capabilities in learning a strong face representation. We further show TCBP has exceptional transfer abilities to applications such as multimodal video clip representation that jointly encodes images, audio, video and text, and video classification. All of these contributions are demonstrated on benchmark video clustering datasets: The Big Bang Theory, Buffy the Vampire Slayer and Harry Potter 1. We provide extensive evaluations on these datasets achieving a significant boost in performance over the base features, and in comparison to the state-of-the-art results

A CONDITIONAL RANDOM FIELD APPROACH FOR AUDIO-VISUAL PEOPLE DIARIZATION (1,2)

We investigate the problem of audio-visual (AV) person diarization in broadcast data. That is, automatically associate the faces and voices of people and determine when they appear or speak in the video. The contributions are twofolds. First, we formulate the problem within a novel CRF framework that simultaneously performs the AV association of voices and face clusters to build AV person models, and the joint segmentation of the audio and visual streams using a set of AV cues and their association strength. Secondly, we use for this AV association strength a score that does not only rely on lips activity, but also on contextual visual information (face size, position, number of detected faces,...) that leads to more reliable association measures. Experiments on 6 hours of broadcast data show that our framework is able to improve the AV-person diarization especially for speaker segments erroneously labeled in the mono-modal case