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A phoneme recognition framework based on auditory spectro-temporal receptive fields

A phoneme recognition framework based on auditory spectro-te...

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作者： Thomas, Samuel Patil, Kailash Ganapathy, Sriram Mesgarani, Nima Hermansky, Hynek Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore United States

We propose to incorporate features derived using spectro-temporal receptive fields (STRFs) of neurons in the auditory cortex for phoneme recognition. Each of these STRFs is tuned to different auditory frequencies, scales and modulation rates. We select different sets of STRFs which are specific for phonemes in different broad phonetic classes (BPC) of sounds. These STRFs are then used as spectro-temporal filters on spectrograms of speech to extract features for phoneme recognition. For the phoneme recognition task on the TIMIT database, the proposed features show a relative improvement of about 5% over conventional feature extraction techniques. © 2010 ISCA.

关键词： speech communication

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Cross-lingual and multi-stream posterior features for low resource LVCSR systems

Cross-lingual and multi-stream posterior features for low re...

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作者： Thomas, Samuel Ganapathy, Sriram Hermansky, Hynek Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore United States

We investigate approaches for large vocabulary continuous speech recognition (LVCSR) system for new languages or new domains using limited amounts of transcribed training data. In these low resource conditions, the performance of conventional LVCSR systems degrade significantly. We propose to train low resource LVCSR system with additional sources of information like annotated data from other languages (German and Spanish) and various acoustic feature streams (short-term and modulation features). We train multilayer perceptrons (MLPs) on these sources of information and use Tandem features derived from the MLPs for low resource LVCSR. In our experiments, the proposed system trained using only one hour of English conversational telephone speech (CTS) provides a relative improvement of 11% over the baseline system. © 2010 ISCA.

关键词： speech communication

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BALANCING FALSE ALARMS AND HITS IN SPOKEN TERM DETECTION

BALANCING FALSE ALARMS AND HITS IN SPOKEN TERM DETECTION

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IEEE International Conference on Acoustics, speech, and Signal processing

作者： Carolina Parada Abhinav Sethy Bhuvana Ramabhadran Human Language Technology Center of Excellence and Center for Language and Speech Processing Johns Hopkins University 3400 North Charles Street Baltimore MD 21210 USA IBM T.J. Watson Research Center Yorktown Heights N.Y. 10568 USA

ISBN: (纸本)9781424442959

This paper presents methods to improve retrieval of Out-Of-Vocabulary (OOV) terms in a Spoken Term Detection (STD) system. We demonstrate that automated tagging of OOV regions helps to reduce false alarms while incorporating phonetic confusability increases the hits. Additional features that boost the probability of a hit in accordance with the number of neighboring hits for the same query and query-length normalization also improve the overall performance of the spoken-term detection system. We show that these methods can be combined effectively to provide a relative improvement of 21percent in Average Term Weighted Value (ATWV) on a 100-hour corpus with 1290 OOV-only queries and 2percent relative on the NIST 2006 STD task, where only 16 of the 1107 queries were OOV terms. Lastly, we present results to show that the proposed methods are general enough to work well in query-by-example based spoken-term detection, and in mismatched situations when the representation of the index being searched through and the queries are not generated by the same system.

关键词： Spoken Term Detection OOV Detection False alarms Sexually Transmitted Diseases Query by example indium

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An evaluation of technologies for knowledge base population 7

An evaluation of technologies for knowledge base population

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7th International Conference on language Resources and Evaluation, LREC 2010

作者： McNamee, Paul Dang, Hoa Trang Simpson, Heather Schone, Patrick Strassel, Stephanie M. Johns Hopkins University Human Language Technology Center of Excellence United States National Institute of Standards and Technology United States Linguistic Data Consortium University of Pennsylvania United States US Department of Defens United States

ISBN: (纸本)2951740867

Previous content extraction evaluations have neglected to address problems which complicate the incorporation of extracted information into an existing knowledge base. Previous question answering evaluations have likewise avoided tasks such as explicit disambiguation of target entities and handling a fixed set of questions about entities without previous determination of possible answers. In 2009 NIST conducted a Knowledge Base Population track at its Text Analysis Conference to unite the content extraction and question answering communities and jointly explore some of these issues. This exciting new evaluation attracted 13 teams from 6 countries that submitted results in two tasks, Entity Linking and Slot Filling. This paper explains the motivation and design of the tasks, describes the language resources that were developed for this evaluation, offers comparisons to previous community evaluations, and briefly summarizes the performance obtained by systems. We also identify relevant issues pertaining to target selection, challenging queries, and performance measures.

关键词： Extraction

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Annotating named entities in Twitter data with crowdsourcing

Annotating named entities in Twitter data with crowdsourcing

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2010 Workshop on Creating speech and language Data with Amazon's Mechanical Turk, Mturk 2010 at the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2010

作者： Finin, Tim Murnane, Will Karandikar, Anand Keller, Nicholas Martineau, Justin Dredze, Mark Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD21250 United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21211 United States

We describe our experience using both Amazon Mechanical Turk (MTurk) and CrowdFlower to collect simple named entity annotations for Twitter status updates. Unlike most genres that have traditionally been the focus of named entity experiments, Twitter is far more informal and abbreviated. The collected annotations and annotation techniques will provide a first step towards the full study of named entity recognition in domains like Facebook and Twitter. We also briefly describe how to use MTurk to collect judgements on the quality of "word clouds. © 2010 Association for Computational Linguistics

关键词： Social networking (online)

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Using web-scale N-grams to improve base NP parsing performance

Using web-scale N-grams to improve base NP parsing performan...

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Pitler, Emily Bergsma, Shane Lin, Dekang Church, Kenneth Computer and Information Science University of Pennsylvania United States Department of Computing Science University of Alberta Canada Google Inc. United States Human Language Technology Center of Excellence Johns Hopkins University United States

We use web-scale N-grams in a base NP parser that correctly analyzes 95.4% of the base NPs in natural text. Web-scale data improves performance. That is, there is no data like more data. Performance scales log-linearly with the number of parameters in the model (the number of unique N-grams). The web-scale N-grams are particularly helpful in harder cases, such as NPs that contain conjunctions.

关键词： Computational linguistics

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Establishing a human language technology center of excellence

Establishing a human language technology center of excellenc...

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作者： Strong, G. Eisner, J. Piatko, C. JHU Human Language Technology Center of Excellence Baltimore MD United States JHU Whiting School of Engineering Baltimore MD United States JHU Applied Physics Laboratory Laurel MD United States

JHU was awarded a long-term contract in January, 2007 to establish and operate a human language technology center of excellence (HLTCOE) near the JHU Homewood campus. The HLTCOE's research focused on advanced technology for automatically analyzing a wide range of speech text and document image data in multiple languages. The focus of the technical program was on automatic population of knowledge bases from text, proof-of-context experiments for robust speech technology and stream characterization from content. These projects addressed key issues in extracting information from large sources of text and speech.

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International Conference on Asian language processing (IALP)

作者： Rejwanul Haque Sudip Kumar Naskar Andy Way Marta R. Costa-jussa Rafael E. Banchs CNGL School of Computing University of Dublin Dublin Ireland Speech and language Barcelona Media Research Center Barcelona Spain Institute of Infocomm Research Human Language Technology A-STAR Singapore

Target phrase selection, a crucial component of the state-of-the-art phrase-based statistical machine translation(PBSMT) model, plays a key role in generating accurate translation hypotheses. Inspired by context-rich word-sense disambiguation techniques, machine translation (MT) researchers have successfully integrated various types of source language context into the PBSMT model to improve target phrase selection. Among the various types of lexical and syntactic features, lexical syntactic descriptions in the form of super tags that preserve long-range word-to-word dependencies in a sentence have proven to be effective. These rich contextual features are able to disambiguate a source phrase, on the basis of the local syntactic behaviour of that phrase. In addition to local contextual information, global contextual information such as the grammatical structure of a sentence, sentence length and n-gram word sequences could provide additional important information to enhance this phrase-sense disambiguation. In this work, we explore various sentence similarity features by measuring similarity between a source sentence to be translated with the source-side of the bilingual training sentences and integrate them directly into the PBSMT model. We performed experiments on an English-to-Chinese translation task by applying sentence-similarity features both individually, and collaboratively with super tag-based features. We evaluate the performance of our approach and report a statistically significant relative improvement of 5.25% BLEU score when adding a sentence-similarity feature together with a super tag-based feature.

关键词： Training Context Context modeling Syntactics Grammar Feature extraction Computational modeling

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Using Web-scale N-grams to Improve Base NP Parsing Performance

Using Web-scale N-grams to Improve Base NP Parsing Performan...

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计算语言学第23届国际学术会议

作者： Emily Pitler Shane Bergsma Kenneth Church Computer and Information Science University of Pennsylvania Department of Computing Science University of Alberta Human Language Technology Center of Excellence Johns Hopkins University

We use web-scale N-grams in a base NP parser that correctly analyzes 95.4%of the base NPs in natural ***-scale data improves *** is,there is no data like more *** scales log-linearly with the number of parameters in the model(the number of unique N-grams).The web-scale N-grams are particularly helpful in harder cases,such as NPs that contain conjunctions.

关键词： Using Web-scale N-grams to Improve Base NP Parsing Performance Web

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JHU Ad Hoc Experiments at CLEF 2008

JHU Ad Hoc Experiments at CLEF 2008

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9th Workshop of the Cross-language Evaluation Forum (CLEF 2008)

作者： McNamee, Paul JHU Human Language Technology Center of Excellence United States

ISBN: (纸本)9783642044465

For CLEF 2008 JHU conducted monolingual and bilingual experiments in the ad hoc TEL and Persian tasks. Additionally we performed several post hoc experiments using previous CLEF ad hoc tests sets in 13 languages. In all three tasks we explored alternative methods of tokenizing documents including plain words, stemmed words, automatically induced segments, a single selected n-gram from each word, and all n-grams from words (i.e., traditional character n-grams). Character n-grams demonstrated consistent gains over ordinary words in each of these three diverse sets of experiments. Using mean average precision, relative gains of of 50-200% on the TEL task, 5% on the Persian task, and 18% averaged over 13 languages from past CLEF evaluations, were observed.

关键词： Character n-grams Farsi language retrieval Multilingual text retrieval

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