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Approaches for Language Identification in Mismatched Environments

Date:
December 13, 2016
Published in:
Proceedings of SLT 2016, San Diego, Calif.
Type:
Conference Paper

Summary

In this paper, we consider the task of language identification in the context of mismatch conditions. Specifically, we address the issue of using unlabeled data in the domain of interest to improve the performance of a state-of-the-art system.

I-Vector Speaker and Language Recognition System on Android,

Date:
September 13, 2016
Published in:
Proceedings of IEEE High Performance Extreme Computing Conference (HPEC '16)
Type:
Conference Paper

Summary

I-Vector based speaker and language identification provides state of the art performance. However, this comes as a more computationally complex solution, which can often lead to challenges in resource-limited devices, such as phones or tablets. We present the implementation of an I-Vector speaker and language recognition system on the Android platform in the form of a fully functional application that allows speaker enrollment and language/speaker scoring within mobile contexts.

Language Recognition via Sparse Coding(354.13 KB)

Date:
September 8, 2016
Published in:
Proceedings of Interspeech 2016, San Francisco, Calif.
Type:
Conference Paper

Summary

Spoken language recognition requires a series of signal processing steps and learning algorithms to model distinguishing characteristics of different languages. In this paper, we present a sparse discriminative feature learning framework for language recognition. We use sparse coding, an unsupervised method, to compute efficient representations for spectral features from a speech utterance while learning basis vectors for language models.

The MITLL NIST LRE 2015 Language Recognition System

Date:
June 21, 2016
Published in:
Proceedings of Odyssey 2016, Bilbao, Spain
Type:
Conference Paper

Summary

In this paper we describe the most recent MIT Lincoln Laboratory language recognition system developed for the NIST 2015 Language Recognition Evaluation (LRE). The submission features a fusion of five core classifiers, with most systems developed in the context of an i-vector framework.

A Fun and Engaging Interface for Crowdsourcing Named Entities(275.07 KB)

Date:
May 23, 2016
Published in:
Proceedings of Tenth International Conference on Language Resources and Evaluation (LREC 2016)
Type:
Conference Paper

Summary

In this paper, we provide a case study in using crowd sourcing to curate an in-domain corpus for named entity recognition, a common problem in natural language processing. In particular, we present our use of fun, engaging user interfaces as a way to entice workers to partake in our crowd sourcing task while avoiding inflating our payments in a way that would attract more mercenary workers than conscientious ones.

A Reverse Approach to Named Entity Extraction and Linking in Microposts(370.29 KB)

Date:
April 11, 2016
Published in:
Proceedings of 6th workshop on Making Sense of Microposts (#Microposts2016)
Type:
Conference Paper

Summary

In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions that are not co-referent with any entities in the knowledge base.

Named Entity Recognition in 140 Characters or Less(158.84 KB)

Date:
April 11, 2016
Published in:
Proceedings of 6th workshop on Making Sense of Microposts (#Microposts2016)
Type:
Conference Paper

Summary

In this paper, we explore the problem of recognizing named entities in microposts, a genre with notoriously little context surrounding each named entity and inconsistent use of grammar, punctuation, capitalization, and spelling conventions by authors. This paper presents the MIT Information Extraction Toolkit (MITIE) and explores its adaptability to the micropost genre.

Deep Neural Network Approaches to Speaker and Language Recognition(323.6 KB)

Date:
October 1, 2015
Published in:
IEEE Signal Processing Letters, vol. 22, no. 10
Type:
Journal Article

Summary

The impressive gains in performance obtained using deep neural networks (DNNs) for automatic speech recognition (ASR) have motivated the application of DNNs to other speech technologies such as speaker recognition (SR) and language recognition (LR). In this work we present the application of single DNN for both SR and LR using the 2013 Domain Adaptation Challenge speaker recognition (DAC13)and the NIST 2011 language recognition evaluation (LRE11) benchmarks.

A Unified Deep Neural Network for Speaker and Language Recognition(254.34 KB)

Date:
September 8, 2015
Published in:
Proceedings of Interspeech 2015, Dresden, Germany
Type:
Conference Paper

Summary

Significant performance gains have been reported separately for speaker recognition (SR) and language recognition (LR) tasks using either DNN posteriors of sub-phonetic units or DNN feature representations, but the two techniques have not been compared on the same SR or LR task or across SR and LR tasks using the same DNN. In this work we present the application of a single DNN for both tasks using the 2013 Domain Adaptation Challenge speaker recognition (DAC13) and the NIST 2011 language recognition evaluation (LRE11) benchmarks.

Finding Good Enough: A Task-Based Evaluation of Query Biased Summarization for Cross-Language Information Retrieval(249.37 KB)

Date:
October 25, 2014
Published in:
Proceedings of Empirical Methods in Natural Language Processing, Doha, Qatar
Type:
Conference Paper

Summary

In this paper we present our task-based evaluation of query biased summarization for cross-language information retrieval (CLIR) using relevance prediction. We describe our 13 summarization methods each from one of four summarization strategies.

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