No abstract

This paper describes recent advances at LIMSI in Mandarin Chinese speech-to-text transcription. A number of novel approaches were introduced in the different system components. The acoustic models are trained on over 1600 hours of audio data from a range of sources, and include pitch and MLP features. N-gram and neural network language models are trained on very large corpora, over 3 billion words of texts; and LM adaptation was explored at different adaptation levels: per show, per snippet, or per speaker cluster. Character-based consensus decoding was found to outperform word-based consensus decoding for Mandarin. The improved system reduces the relative character error rate (CER) by about 10% on previous GALE development and evaluation data sets, obtaining a CER of 9.2% on the P4 broadcast news and broadcast conversation evaluation data.

This paper describes our development work to design a language recognition system that can discriminate closely related languages and dialects of the same language. The work was a joint effort by LIMSI and Vocapia Research in preparation for the NIST 2015 Language Recognition Evaluation (LRE). The language recognition system results from a fusion of four core classifiers: a phonotactic component using DNN acoustic models, two purely acoustic components using a RNN model and i-vector model, and a lexical component. Each component generates language posterior probabilities optimized to maximize the LID NCE, making their combination simple and robust. The motivation for using multiple components representing different speech knowledge is that some dialect distinctions may not be manifest at the acoustic level. We report experiments on the NIST LRE15 data and provide an analysis of the results and some post-evaluation contrasts. The 2015 LRE task focused on the identification of 20 languages clustered in 6 groups (Arabic, Chinese, English, French, Slavic and Iberic) of similar languages. Results are reported using the NIST Cavg metric which served as the primary metric for the OpenLRE15 evaluation. Results are also reported for the EER and the LER.

Poster session: WS21 - Workshop on Information Fusion in Computer Vision for Concept RecognitionInternational audienceThe REPERE challenge is a project aiming at the evaluation of systems for supervised and unsupervised multimodal recognition of people in TV broadcast. In this paper, we describe, evaluate and discuss QCompere consortium submissions to the 2012 REPERE evaluation campaign dry-run. Speaker identification (and face recognition) can be greatly improved when combined with name detection through video optical character recognition. Moreover, we show that unsupervised multimodal person recognition systems can achieve performance nearly as good as supervised monomodal ones (with several hundreds of identity models)