QCompere @ REPERE 2013

International audienceWe describe QCompere consortium submissions to the REPERE 2013 evaluation campaign. The REPERE challenge aims at gathering four communities (face recognition, speaker identification, optical character recognition and named entity detection) towards the same goal: multimodal person recognition in TV broadcast. First, four mono-modal components are introduced (one for each foregoing community) constituting the elementary building blocks of our various submissions. Then, depending on the target modality (speaker or face recognition) and on the task (supervised or unsupervised recognition), four different fusion techniques are introduced: they can be summarized as propagation-, classifier-, rule- or graph-based approaches. Finally, their performance is evaluated on REPERE 2013 test set and their advantages and limitations are discussed

Unsupervised Speaker Identification in TV Broadcast Based on Written Names

International audienceIdentifying speakers in TV broadcast in an unsuper- vised way (i.e. without biometric models) is a solution for avoiding costly annotations. Existing methods usually use pronounced names, as a source of names, for identifying speech clusters provided by a diarization step but this source is too imprecise for having sufficient confidence. To overcome this issue, another source of names can be used: the names written in a title block in the image track. We first compared these two sources of names on their abilities to provide the name of the speakers in TV broadcast. This study shows that it is more interesting to use written names for their high precision for identifying the current speaker. We also propose two approaches for finding speaker identity based only on names written in the image track. With the "late naming" approach, we propose different propagations of written names onto clusters. Our second proposition, "Early naming", modifies the speaker diarization module (agglomerative clustering) by adding constraints preventing two clusters with different associated written names to be merged together. These methods were tested on the REPERE corpus phase 1, containing 3 hours of annotated videos. Our best "late naming" system reaches an F-measure of 73.1%. "early naming" improves over this result both in terms of identification error rate and of stability of the clustering stopping criterion. By comparison, a mono-modal, supervised speaker identification system with 535 speaker models trained on matching development data and additional TV and radio data only provided a 57.2% F-measure

QCompere @ REPERE 2013

International audienceWe describe QCompere consortium submissions to the REPERE 2013 evaluation campaign. The REPERE challenge aims at gathering four communities (face recognition, speaker identification, optical character recognition and named entity detection) towards the same goal: multimodal person recognition in TV broadcast. First, four mono-modal components are introduced (one for each foregoing community) constituting the elementary building blocks of our various submissions. Then, depending on the target modality (speaker or face recognition) and on the task (supervised or unsupervised recognition), four different fusion techniques are introduced: they can be summarized as propagation-, classifier-, rule- or graph-based approaches. Finally, their performance is evaluated on REPERE 2013 test set and their advantages and limitations are discussed

Speaker tracking system using speaker boundary detection

This thesis is about a research conducted in the area of Speaker Recognition. The application is concerned to the automatic detection and tracking of target speakers in meetings, conferences, telephone conversations and in radio and television broadcasts. A Speaker Tracking system is developed here, in collaboration with the Center for Language and Speech Technologies and Applications (TALP) in UPC. The main objective of this Speaker Tracking system is to answer the question: When the target speaker speaks? The system uses training speech data for the target speaker in the pre-enrollment stage. Three main modules have been designed for this Speaker Tracking system. In the first module an energy based Speech Activity Detection is applied to select the speech parts of the audio. In the second module the audio is segmented according to the speaker turning points. In the last module a Speaker Verification is implemented in which the target speakers are verified and tracked. Two different approaches are applied in this last module. In the first approach for Speaker Verification, the target speakers and the segments are modeled using the state-of-the-art, Gaussian Mixture Models (GMM). In the second approach for Speaker Verification, the identity vectors (i-vectors) representation is applied for the target speakers and the segments. Finally, the performance of both these approaches is compared for the results evaluation

Unsupervised Speaker Identification using Overlaid Texts in TV Broadcast

Poster Session: Speaker Recognition IIIInternational audienceWe propose an approach for unsupervised speaker identification in TV broadcast videos, by combining acoustic speaker diarization with person names obtained via video OCR from overlaid texts. Three methods for the propagation of the overlaid names to the speech turns are compared, taking into account the co-occurence duration between the speaker clusters and the names provided by the video OCR and using a task-adapted variant of the TF-IDF information retrieval coefficient. These methods were tested on the REPERE dry-run evaluation corpus, containing 3 hours of annotated videos. Our best unsupervised system reaches a F-measure of 70.2% when considering all the speakers, and 81.7% if anchor speakers are left out. By comparison, a mono-modal, supervised speaker identification system with 535 speaker models trained on matching development data and additional TV and radio data only provided a 57.5% F-measure when considering all the speakers and 45.7% without anchor

Evaluating automatic speaker recognition systems: an overview of the nist speaker recognition evaluations (1996-2014)

2014 CSIC. Manuscripts published in this Journal are the property of the Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.Automatic Speaker Recognition systems show interesting properties, such as speed of processing or repeatability of results, in contrast to speaker recognition by humans. But they will be usable just if they are reliable. Testability, or the ability to extensively evaluate the goodness of the speaker detector decisions, becomes then critical. In the last 20 years, the US National Institute of Standards and Technology (NIST) has organized, providing the proper speech data and evaluation protocols, a series of text-independent Speaker Recognition Evaluations (SRE). Those evaluations have become not just a periodical benchmark test, but also a meeting point of a collaborative community of scientists that have been deeply involved in the cycle of evaluations, allowing tremendous progress in a specially complex task where the speaker information is spread across different information levels (acoustic, prosodic, linguistic…) and is strongly affected by speaker intrinsic and extrinsic variability factors. In this paper, we outline how the evaluations progressively challenged the technology including new speaking conditions and sources of variability, and how the scientific community gave answers to those demands. Finally, NIST SREs will be shown to be not free of inconveniences, and future challenges to speaker recognition assessment will also be discussed

Speaker Diarization

Disertační práce se zaměřuje na téma diarizace řečníků, což je úloha zpracování řeči typicky charakterizovaná otázkou "Kdo kdy mluví?". Práce se také zabývá související úlohou detekce překrývající se řeči, která je velmi relevantní pro diarizaci. Teoretická část práce poskytuje přehled existujících metod diarizace řečníků, a to jak těch offline, tak online, a přibližuje několik problematických oblastí, které byly identifikovány v rané fázi autorčina výzkumu. V práci je také předloženo rozsáhlé srovnání existujících systémů se zaměřením na jejich uváděné výsledky. Jedna kapitola se také zaměřuje na téma překrývající se řeči a na metody její detekce. Experimentální část práce předkládá praktické výstupy, kterých bylo dosaženo. Experimenty s diarizací se zaměřovaly zejména na online systém založený na GMM a na i-vektorový systém, který měl offline i online varianty. Závěrečná sekce experimentů také přibližuje nově navrženou metodu pro detekci překrývající se řeči, která je založena na konvoluční neuronové síti.ObhájenoThe thesis focuses on the topic of speaker diarization, a speech processing task that is commonly characterized as the question "Who speaks when?". It also addresses the related task of overlapping speech detection, which is very relevant for diarization. The theoretical part of the thesis provides an overview of existing diarization approaches, both offline and online, and discusses some of the problematic areas which were identified in early stages of the author's research. The thesis also includes an extensive comparison of existing diarization systems, with focus on their reported performance. One chapter is also dedicated to the topic of overlapping speech and the methods of its detection. The experimental part of the thesis then presents the work which has been done on speaker diarization, which was focused mostly on a GMM-based online diarization system and an i-vector based system with both offline and online variants. The final section also details a newly proposed approach for detecting overlapping speech using a convolutional neural network

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book