Juliette Kahn

A presentation of the REPERE challenge

The REPERE Challenge aims to support research on people recognition in multimodal conditions. To assess the technology progress, annual evaluation campaigns will be organized from 2012 to 2014. In this context the REPERE corpus, a French video corpus with multimodal annotation, has been developed. The systems have to answer the following questions: Who is speaking? Who is present in the video? What names are cited? What names are displayed? The challenge is to combine the various information coming from the speech and the images.

The Maurdor Project: Improving Automatic Processing of Digital Documents

This paper presents the achievements of an experimental project called Maurdor (Moyens AUtomatisés deReconnaissance de Documents ecRits - Automatic Processingof Digital Documents) funded by the French DGA that aims at improving processing technologies for handwritten and typewritten documents in French, English and Arabic. The first part describes the context and objectives of the project. The second part deals with the challenge of creating a realistic corpus of 10,000 annotated documents to support the efficient development and evaluation of processing modules. The third part presents the organisation, metric definition and results of the Maurdor International evaluation campaign. The last part presents the Maurdor demonstrator with a functional and technical perspective.

Speaker verification by inexperienced and experienced listeners vs. speaker verification system

This paper describes the participation of the LIA in the Human Assisted Speaker Recognition (HASR) task of the NIST-SRE 2010 evaluation campaign and its extension to a larger number of listeners. The human performance in such unfavorable conditions is analyzed in relation to the decision of a speaker recognition automatic system. Results of the perception test showed an important inter-trial variability (from 3% to 90% of correct answers for non-target trials) whereas there was no significant difference between the experienced and inexperienced listeners. Some complementarity between speaker verification system and human decisions was also found.

First maurdor 2013 evaluation campaign in scanned document image processing

This paper presents the results of the first Maurdor evaluation campaign. This campaign aims at evaluating the complete chain of scanned document image processing. It has a modular structure that includes page segmentation and zone classification, identification of writing type and language, optical character recognition and revealing logical structure of a document. This campaign is based on a unique corpus of 8,000 images of scanned documents annotated at different levels. Presentation of the results of the first campaign is important to assess the state-of-the-art and create common references both for participants in future campaigns and, as the scoring tools are publicly available, for independent tests.

Homogeneity Measure for Forensic Voice Comparison: A Step Forward Reliability

In forensic voice comparison, it is strongly recommended to follow the Bayesian paradigm to present a forensic evidence to the court. In this paradigm, the strength of the forensic evidence is summarized by a likelihood ratio (LR). But in the real world, to base only on the LR without looking to its degree of reliability does not allow experts to have a good judgement. This work is mainly motivated by the need to quantify this reliability. In this concept, we think that the presence of speaker specific information and its homogeneity between the two signals to compare should be evaluated. This paper is dedicated to the latter, the homogeneity. We propose an information theory based homogeneity measure which determines whether a voice comparison is feasible or not.

The zonemap metric for page segmentation and area classification in scanned documents

A novel metric for the detection and classification of different areas in scanned documents is presented in this paper. This metric, ZoneMap, aims at evaluating both page segmentation and zone classification. Moreover, for the segmentation sub-task, it handles the superposition of overlapping zones. These characteristics allow to evaluate systems in a coherent way using a single ZoneMap metric. Weights assigned to different parameters add flexibility and allow for fine-tuning of the metric in order to reflect the specificity of a particular applicative context. ZoneMap was experimented in the Maurdor evaluation campaigns where it is used as a primary metric for page segmentation and area classification. Evaluation results show that ZoneMap provides additional ways to assess system performance and analyze the results. ZoneMap is implemented in a publicly available LNE maurdor-eval evaluation toolkit that is distributed under the GPL license.

Phonological content impact on wrongful convictions in Forensic Voice Comparison context

Forensic Voice Comparison (FVC) is increasingly using the likelihood ratio (LR) in order to indicate whether the evidence supports the prosecution (same-speaker) or defender (different-speakers) hypotheses. Nevertheless, the LR accepts some practical limitations due both to its estimation process itself and to a lack of knowledge about the reliability of this (practical) estimation process. It is particularly true when FVC is considered using Automatic Speaker Recognition (ASR) systems. Indeed, in the LR estimation performed by ASR systems, different factors are not considered such as speaker intrinsic characteristics, denoted “speaker factor”, the amount of information involved in the comparison as well as the phonological content and so on. This article focuses on the impact of phonological content on FVC involving two different speakers and more precisely the potential implication of a specific phonemic category on wrongful conviction cases (innocents are send behind bars). We show that even though the vast majority of speaker pairs (more than 90%) are well discriminated, few pairs are difficult to distinguish. For the “best” discriminated pairs, all the phonemic content play a positive role in speaker discrimination while for the “worst” pairs, it appears that nasals have a negative effect and lead to a confusion between speakers.

Beyond Doddington menagerie, a first step towards

During the last decade, speaker verification systems have shown significant progress and have reached a level of performance and accuracy that support their utilization in practical applications, including the forensic ones. This context emphasizes the importance of a deeper analysis of the systems performance over basic error rate. In this paper, the influence of the speaker (his/her ‘voice’) on the performance is studied and the effect of the model (the training excerpt) is investigated. The experimental setup is based on an open source system and the experimental context of NIST-SRE 2008. The results confirm that the lower performances are obtained from a reduced number of speakers. Even more than speaker factor, speaker verification system performances are shown to be highly dependant on the voice samples used to train speaker models.

Speaker specific features and phonemes in speech: a proposal for evaluating a possible interaction

Speaker voice characteristics are an important aspect of forensic phonetics. Previous studies have suggested that all the features present in the speech signals are not equally important for speaker discrimination, and it is well-known that subsets of phonemes are more informative than others. However, most of theses studies have concerned a whole group of speakers, without taking into account the speaker specificities. This paper presents a framework for the selection of a subset of phonemes from the speech signal at the speaker level in order to capture the speaker variability in this selection process. We present the approach for the selection of the most discriminatory phonemes and a preliminary study have been performed on French reading speech database. At the global level, the most discriminatory phonemes are compared to previous studies. At the speaker level, we have examined the inter-speaker variability according to their most discriminatory phonemes. The experimental results verified the effectiveness of the proposed framework.