We propose a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework. To evaluate the performance of our model, we introduce a new dataset consisting of six episodes of the Big Bang Theory TV show and eighteen full movies covering different genres. Our experiments show that our multimodal model significantly outperforms several competitive baselines on the average weighted F-score metric. To demonstrate the effectiveness of our framework, we design an end-to-end memory network model that leverages our speaker naming model and achieves state-of-the-art results on the subtitles task of the MovieQA 2017 Challenge.
The problem of speaker naming in movies has been explored by the computer vision and the speech communities. In the computer vision community, the speaker naming problem is usually considered as a face person naming problem, in which names are assigned to their corresponding faces on the screen @cite_31 @cite_20 @cite_38 @cite_11 @cite_35 . On the other hand, the speech community considered the problem as a speaker identification problem, which focuses on recognizing and clustering speakers rather than naming them @cite_33 @cite_21 . In this work, we aim to solve the problem of speaker naming in movies, in which we label each segment of the subtitles with its corresponding speaker name whether the speaker's face appeared on in the video or not.
@cite_35 @cite_38 @cite_33 @cite_21 @cite_31 @cite_20 @cite_11
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Automatic speaker naming is the problem of localizing as well as identifying each speaking character in a TV movie live show video. This is a challenging problem mainly attributes to its multimodal nature, namely face cue alone is insufficient to achieve good performance. Previous multimodal approaches to this problem usually process the data of different modalities individually and merge them using handcrafted heuristics. Such approaches work well for simple scenes, but fail to achieve high performance for speakers with large appearance variations. In this paper, we propose a novel convolutional neural networks (CNN) based learning framework to automatically learn the fusion function of both face and audio cues. We show that without using face tracking, facial landmark localization or subtitle transcript, our system with robust multimodal feature extraction is able to achieve state-of-the-art speaker naming performance evaluated on two diverse TV series. The dataset and implementation of our algorithm are publicly available online. We address the character identification problem in movies and television videos: assigning names to faces on the screen. Most prior work on person recognition in video assumes some supervised data such as screenplay or handlabeled faces. In this paper, our only source of ‘supervision’ are the dialog cues: first, second and third person references (such as “I'm Jack”, “Hey, Jack!” and “Jack left”). While this kind of supervision is sparse and indirect, we exploit multiple modalities and their interactions (appearance, dialog, mouth movement, synchrony, continuity-editing cues) to effectively resolve identities through local temporal grouping followed by global weakly supervised recognition. We propose a novel temporal grouping model that partitions face tracks across multiple shots while respecting appearance, geometric and film-editing cues and constraints. In this model, states represent partitions of the k most recent face tracks, and transitions represent compatibility of consecutive partitions. We present dynamic programming inference and discriminative learning for the model. The individual face tracks are subsequently assigned a name by learning a classifier from partial label constraints. The weakly supervised classifier incorporates multiple-instance constraints from dialog cues as well as soft grouping constraints from our temporal grouping. We evaluate both the temporal grouping and final character naming on several hours of TV and movies. Identification of characters in films, although very intuitive to humans, still poses a significant challenge to computer methods. In this paper, we investigate the problem of identifying characters in feature-length films using video and film script. Different from the state-of-the-art methods on naming faces in the videos, most of which used the local matching between a visible face and one of the names extracted from the temporally local video transcript, we attempt to do a global matching between names and clustered face tracks under the circumstances that there are not enough local name cues that can be found. The contributions of our work include: 1) A graph matching method is utilized to build face-name association between a face affinity network and a name affinity network which are, respectively, derived from their own domains (video and script). 2) An effective measure of face track distance is presented for face track clustering. 3) As an application, the relationship between characters is mined using social network analysis. The proposed framework is able to create a new experience on character-centered film browsing. Experiments are conducted on ten feature-length films and give encouraging results. Speaker diarization, usually denoted as the “who spoke when” task, turns out to be particularly challenging when applied to fictional films, where many characters talk in various acoustic conditions (background music, sound effects...). Despite this acoustic variability, such movies exhibit specific visual patterns in the dialogue scenes. In this paper, we introduce a two-step method to achieve speaker diarization in TV series: a speaker diarization is first performed locally in the scenes detected as dialogues; then, the hypothesized local speakers are merged in a second agglomerative clustering process, with the constraint that speakers locally hypothesized to be distinct must not be assigned to the same cluster. The performances of our approach are compared to those obtained by standard speaker diarization tools applied to the same data. We investigate the problem of automatically labelling faces of characters in TV or movie material with their names, using only weak supervision from automatically-aligned subtitle and script text. Our previous work ( [8]) demonstrated promising results on the task, but the coverage of the method (proportion of video labelled) and generalization was limited by a restriction to frontal faces and nearest neighbour classification. In this paper we build on that method, extending the coverage greatly by the detection and recognition of characters in profile views. In addition, we make the following contributions: (i) seamless tracking, integration and recognition of profile and frontal detections, and (ii) a character specific multiple kernel classifier which is able to learn the features best able to discriminate between the characters. We report results on seven episodes of the TV series "Buffy the Vampire Slayer", demonstrating significantly increased coverage and performance with respect to previous methods on this material. Naming every individual person appearing in broadcast news videos with names detected from the video transcript leads to better access of the news video content. In this paper, we approach this challenging problem with a statistical learning method. Two categories of information extracted from multiple video modalities have been explored, namely features, which help distinguish the true name of every person, as well as constraints, which reveal the relationships among the names of different persons. The person-naming problem is formulated into a learning framework which predicts the most likely name for each person based on the features, and refines the predictions using the constraints. Experiments conducted on ABC World New Tonight and CNN Headline News videos demonstrate that this approach outperforms a non-learning alternative by a large amount. Natural language descriptions of videos provide a potentially rich and vast source of supervision. However, the highly-varied nature of language presents a major barrier to its effective use. What is needed are models that can reason over uncertainty over both videos and text. In this paper, we tackle the core task of person naming: assigning names of people in the cast to human tracks in TV videos. Screenplay scripts accompanying the video provide some crude supervision about who’s in the video. However, even the basic problem of knowing who is mentioned in the script is often difficult, since language often refers to people using pronouns (e.g., “he”) and nominals (e.g., “man”) rather than actual names (e.g., “Susan”). Resolving the identity of these mentions is the task of coreference resolution, which is an active area of research in natural language processing. We develop a joint model for person naming and coreference resolution, and in the process, infer a latent alignment between tracks and mentions. We evaluate our model on both vision and NLP tasks on a new dataset of 19 TV episodes. On both tasks, we significantly outperform the independent baselines. Naming faces is important for news videos browsing and indexing. Although some research efforts have been contributed to it, they only use the concurrent information between the face and name or employ some clues as features and use simple heuristic method or machine learning approach to finish the task. They use little extra knowledge about the names and faces. Different from previous work, in this paper we present a novel approach to name the faces by exploring extra knowledge obtained from image google. The behind assumption is that the faces of those important persons will turn out many times in the web images and could be retrieved from image google easily. Firstly, faces are detected in the video frames; and the name entities of candidate persons are extracted from the textual information by automatic speech recognition and close caption detection. Then, these candidate person names are used as queries to find the name related person images through image google. After that, the retrieved result is analyzed and some typical faces are selected through feature density estimation. Finally, the detected faces in the news video are matched with the faces selected from the result returned by image google to label each face. Experimental results on MSNBC news and CNN news demonstrate that the proposed approach is effective. In this paper, we present a system that associates faces with voices in a video by fusing information from the audio and visual signals. The thesis underlying our work is that an extremely simple approach to generating (weak) speech clusters can be combined with visual signals to effectively associate faces and voices by aggregating statistics across a video. This approach does not need any training data specific to this task and leverages the natural coherence of information in the audio and visual streams. It is particularly applicable to tracking speakers in videos on the web where a priori information about the environment (e.g., number of speakers, spatial signals for beamforming) is not available. We performed experiments on a real-world dataset using this analysis framework to determine the speaker in a video. Given a ground truth labeling determined by human rater consensus, our approach had 71 accuracy. Speaker diarization is the task of determining “who spoke when?” in an audio or video recording that contains an unknown amount of speech and also an unknown number of speakers. Initially, it was proposed as a research topic related to automatic speech recognition, where speaker diarization serves as an upstream processing step. Over recent years, however, speaker diarization has become an important key technology for many tasks, such as navigation, retrieval, or higher level inference on audio data. Accordingly, many important improvements in accuracy and robustness have been reported in journals and conferences in the area. The application domains, from broadcast news, to lectures and meetings, vary greatly and pose different problems, such as having access to multiple microphones and multimodal information or overlapping speech. The most recent review of existing technology dates back to 2006 and focuses on the broadcast news domain. In this paper, we review the current state-of-the-art, focusing on research developed since 2006 that relates predominantly to speaker diarization for conference meetings. Finally, we present an analysis of speaker diarization performance as reported through the NIST Rich Transcription evaluations on meeting data and identify important areas for future research. We consider two scenarios of naming people in databases of news photos with captions: (i) finding faces of a single person, and (ii) assigning names to all faces. We combine an initial text-based step, that restricts the name assigned to a face to the set of names appearing in the caption, with a second step that analyzes visual features of faces. By searching for groups of highly similar faces that can be associated with a name, the results of purely text-based search can be greatly ameliorated. We improve a recent graph-based approach, in which nodes correspond to faces and edges connect highly similar faces. We introduce constraints when optimizing the objective function, and propose improvements in the low-level methods used to construct the graphs. Furthermore, we generalize the graph-based approach to face naming in the full data set. In this multi-person naming case the optimization quickly becomes computationally demanding, and we present an important speed-up using graph-flows to compute the optimal name assignments in documents. Generative models have previously been proposed to solve the multi-person naming task. We compare the generative and graph-based methods in both scenarios, and find significantly better performance using the graph-based methods in both cases. The paper addresses the problem of speaker (or voice) de-identification by presenting a novel approach for concealing the identity of speakers in their speech. The proposed technique first recognizes the input speech with a diphone recognition system and then transforms the obtained phonetic transcription into the speech of another speaker with a speech synthesis system. Due to the fact that a Diphone RecOgnition step and a sPeech SYnthesis step are used during the de-identification, we refer to the developed technique as DROPSY. With this approach the acoustical models of the recognition and synthesis modules are completely independent from each other, which ensures the highest level of input speaker de-identification. The proposed DROPSY-based de-identification approach is language dependent, text independent and capable of running in real-time due to the relatively simple computing methods used. When designing speaker de-identification technology two requirements are typically imposed on the de-identification techniques: i) it should not be possible to establish the identity of the speakers based on the de-identified speech, and ii) the processed speech should still sound natural and be intelligible. This paper, therefore, implements the proposed DROPSY-based approach with two different speech synthesis techniques (i.e, with the HMM-based and the diphone TD-PSOLA-based technique). The obtained de-identified speech is evaluated for intelligibility and evaluated in speaker verification experiments with a state-of-the-art (i-vector PLDA) speaker recognition system. The comparison of both speech synthesis modules integrated in the proposed method reveals that both can efficiently de-identify the input speakers while still producing intelligible speech. We describe a probabilistic method for identifying characters in TV series or movies. We aim at labeling every character appearance, and not only those where a face can be detected. Consequently, our basic unit of appearance is a person track (as opposed to a face track). We model each TV series episode as a Markov Random Field, integrating face recognition, clothing appearance, speaker recognition and contextual constraints in a probabilistic manner. The identification task is then formulated as an energy minimization problem. In order to identify tracks without faces, we learn clothing models by adapting available face recognition results. Within a scene, as indicated by prior analysis of the temporal structure of the TV series, clothing features are combined by agglomerative clustering. We evaluate our approach on the first 6 episodes of The Big Bang Theory and achieve an absolute improvement of 20 for person identification and 12 for face recognition. Speaker verification systems traditionally extract and model cepstral features or filter bank energies from the speech signal. In this paper, inspired by the success of neural network-based approaches to model directly raw speech signal for applications such as speech recognition, emotion recognition and anti-spoofing, we propose a speaker verification approach where speaker discriminative information is directly learned from the speech signal by: (a) first training a CNN-based speaker identification system that takes as input raw speech signal and learns to classify on speakers (unknown to the speaker verification system); and then (b) building a speaker detector for each speaker in the speaker verification system by replacing the output layer of the speaker identification system by two outputs (genuine, impostor), and adapting the system in a discriminative manner with enrollment speech of the speaker and impostor speech data. Our investigations on the Voxforge database shows that this approach can yield systems competitive to state-of-the-art systems. An analysis of the filters in the first convolution layer shows that the filters give emphasis to information in low frequency regions (below 1000 Hz) and implicitly learn to model fundamental frequency information in the speech signal for speaker discrimination. While successful on broadcast news, meetings or telephone conversation, state-of-the-art speaker diarization techniques tend to perform poorly on TV series or movies. In this paper, we propose to rely on state-of-the-art face clustering techniques to guide acoustic speaker diarization. Two approaches are tested and evaluated on the first season of Game Of Thrones TV series. The second (better) approach relies on a novel talking-face detection module based on bi-directional long short-term memory recurrent neural network. Both audio-visual approaches outperform the audio-only baseline. A detailed study of the behavior of these approaches is also provided and paves the way to future improvements. Matching people across views is still an open problem in computer vision and in video surveillance systems. In this paper we address the problem of person re-identification across disjoint cameras by proposing an efficient but robust kernel descriptor to encode the appearance of a person. The matching is then improved by applying a learning technique based on Kernel Canonical Correlation Analysis (KCCA) which finds a common subspace between the proposed descriptors extracted from disjoint cameras, projecting them into a new description space. This common description space is then used to identify a person from one camera to another with a standard nearest-neighbor voting method. We evaluate our approach on two publicly available datasets for re-identification (VIPeR and PRID), demonstrating that our method yields state-of-the-art performance with respect to recent techniques proposed for the re-identification task. The wide availability of large scale databases requires more efficient and scalable tools for data understanding and knowledge discovery. In this paper, we present a method to find important people who have appeared repeatedly in a certain time period from large news video databases. Specifically, we investigate two issues: how to group similar faces to find dominant groups and how to label these groups by the corresponding names for identification. These are challenging problems because firstly people can appear with large appearance variations such as hair styles, illumination conditions and poses that make comparing between similar faces more difficult: secondly, the number of people and their occurrence frequencies that are unknown make finding dominant and useful groups more complicated: and finally, the fact that in news video faces and names usually do not appear together can make troubles in aligning faces and names. To handle above problems, we propose using the relevant set correlation based clustering model which can efficiently handle dataset of millions of objects represented in thousands or even millions of dimensions to find groups of similar faces from the large and noisy face dataset. Then in order to identify faces in clusters, names extracted from the transcripts are filtered and used to find the best correspondences by using methods developed in the statistical machine translation literature. Experiments on large video datasets containing hundreds of hours showed that our system can efficiently find out important people by not only their appearance but also their identification. We are given a set of video clips, each one annotated with an ordered list of actions, such as “walk” then “sit” then “answer phone” extracted from, for example, the associated text script. We seek to temporally localize the individual actions in each clip as well as to learn a discriminative classifier for each action. We formulate the problem as a weakly supervised temporal assignment with ordering constraints. Each video clip is divided into small time intervals and each time interval of each video clip is assigned one action label, while respecting the order in which the action labels appear in the given annotations. We show that the action label assignment can be determined together with learning a classifier for each action in a discriminative manner. We evaluate the proposed model on a new and challenging dataset of 937 video clips with a total of 787720 frames containing sequences of 16 different actions from 69 Hollywood movies. In real-world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blame in digital forensic. To resolve this issue, the name disambiguation task is designed which aims to partition the documents associated with a name reference such that each partition contains documents pertaining to a unique real-life person. Existing solutions to this task substantially rely on feature engineering, such as biographical feature extraction, or construction of auxiliary features from Wikipedia. However, for many scenarios, such features may be costly to obtain or unavailable due to the risk of privacy violation. In this work, we propose a novel name disambiguation method. Our proposed method is non-intrusive of privacy because instead of using attributes pertaining to a real-life person, our method leverages only relational data in the form of anonymized graphs. In the methodological aspect, the proposed method uses a novel representation learning model to embed each document in a low dimensional vector space where name disambiguation can be solved by a hierarchical agglomerative clustering algorithm. Our experimental results demonstrate that the proposed method is significantly better than the existing name disambiguation methods working in a similar setting. We propose a three-stage pixel-based visual front end for automatic speechreading (lipreading) that results in significantly improved recognition performance of spoken words or phonemes. The proposed algorithm is a cascade of three transforms applied on a three-dimensional video region-of-interest that contains the speaker's mouth area. The first stage is a typical image compression transform that achieves a high-energy, reduced-dimensionality representation of the video data. The second stage is a linear discriminant analysis-based data projection, which is applied on a concatenation of a small amount of consecutive image transformed video data. The third stage is a data rotation by means of a maximum likelihood linear transform that optimizes the likelihood of the observed data under the assumption of their class-conditional multivariate normal distribution with diagonal covariance. We applied the algorithm to visual-only 52-class phonetic and 27-class visemic classification on a 162-subject, 8-hour long, large vocabulary, continuous speech audio-visual database. We demonstrated significant classification accuracy gains by each added stage of the proposed algorithm which, when combined, can achieve up to 27 improvement. Overall, we achieved a 60 (49 ) visual-only frame-level visemic classification accuracy with (without) use of test set viseme boundaries. In addition, we report improved audio-visual phonetic classification over the use of a single-stage image transform visual front end. Finally, we discuss preliminary speech recognition results.