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EFFECT OF FILTER BANDWIDTH AND SPECTRAL SAMPLING RATE OF ANALYSIS FILTERBANK ON AUTOMATIC PHONEME RECOGNITION

EFFECT OF FILTER BANDWIDTH AND SPECTRAL SAMPLING RATE OF ANA...

引用

IEEE International Conference on Acoustics, speech and Signal processing

作者： Feipeng Li Hynek Hermansky Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD 21218

ISBN: (纸本)9781479903573

In this study we investigate the effect of filter bandwidth and spectral sampling rate of analysis filterbank for speech recognition. Two experiments are conducted to evaluate the performance of an automatic phoneme recognition system on clean speech and speech in noise as the filter bandwidth increases from 0.5 to 3.5 ERB and the spectral resolution changes from 1,1.5,2, 3,4, to 6 samples per Bark. Results indicate that the optimum filter bandwidth varies for different speech sounds at different frequency ranges. A spectral sampling of 4 filters per Bark with the filter bandwidth being ≈1 ERB produces the best performance on average.

关键词： filter bandwidth spectral resolution phoneme recognition Spectral resolution Bandwidth Filters AUTOMATIC recognition system Bark speech

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WEAK TOP-DOWN CONSTRAINTS FOR UNSUPERVISED ACOUSTIC MODEL TRAINING

WEAK TOP-DOWN CONSTRAINTS FOR UNSUPERVISED ACOUSTIC MODEL TR...

引用

IEEE International Conference on Acoustics, speech, and Signal processing

作者： Aren Jansen Samuel Thomas Hynek Hermansky Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781479903573

Typical supervised acoustic model training relies on strong top-down constraints provided by dynamic programming alignment of the input observations to phonetic sequences derived from orthographic word transcripts and pronunciation dictionaries. This paper investigates a much weaker form of top-down supervision for use in place of transcripts and dictionaries in the zero resource setting. Our proposed constraints, which can be produced using recent spoken term discovery systems, come in the form of pairs of isolated word examples that share the same unknown type. For each pair, we perform a dynamic programming alignment of the acoustic observations of the two constituent examples, generating an inventory of cross-speaker frame pairs that each provide evidence that the same subword unit model should account for them. We find these weak top-down constraints are capable of improving model speaker independence by up to 57% relative over bottom-up training alone.

关键词： speaker independent acoustic models unsupervised training spectral clustering top-down constraints Constraint Dictionaries as Topic top-down Pronunciation transcript dynamic programming Training Isolated words Regulated industries

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FREQUENCY OFFSET CORRECTION IN speech WITHOUT DETECTING PITCH

FREQUENCY OFFSET CORRECTION IN SPEECH WITHOUT DETECTING PITC...

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IEEE International Conference on Acoustics, speech, and Signal processing

作者： Pascal Clark Sri Harish Mallidi Aren Jansen Hynek Hermansky Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University Baltimore Maryland USA

ISBN: (纸本)9781479903573

Radio-transmitted speech sometimes contains a residual frequency shift or offset, resulting from incorrect demodulation in single-sideband channels. Frequency-shifted speech can mask speaker identity and reduce intelligibility. Therefore, frequency offset will degrade the performance of downstream speech technologies. Existing offset correction methods require a pitch estimate of the speech signal, which is difficult in noisy radio channels. We present a new, automatic algorithm for detecting and correcting frequency offset, based on third-order modulation spectral analysis. Our method is remarkably simple and does not require pitch estimation. We provide derivations, examples, and a pilot study demonstrating how offset correction improves speaker verification for radio-transmitted speech.

关键词： Modulation spectrum speech enhancement speaker recognition single-sideband frequency offset FREQUENCY SHIFT Single sideband speaker recognition speech speech enhancement bias calibration modulation spectra offsets PITCHES pitch Voice Signal Pitch estimation Ship motion

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Topic Models and Metadata for Visualizing Text Corpora

Topic Models and Metadata for Visualizing Text Corpora

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2013 Annual Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2013 - Demonstration Session

作者： Snyder, Justin Knowles, Rebecca Dredze, Mark Gormley, Matthew R. Wolfe, Travis Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21211 United States

ISBN: (纸本)9781937284473

Effectively exploring and analyzing large text corpora requires visualizations that provide a high level summary. Past work has relied on faceted browsing of document metadata or on natural language processing of document text. In this paper, we present a new web-based tool that integrates topics learned from an unsupervised topic model in a faceted browsing experience. The user can manage topics, filter documents by topic and summarize views with metadata and topic graphs. We report a user study of the usefulness of topics in our tool. © 2013 Association for Computational Linguistics.

关键词： Metadata

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QUANTIFYING THE VALUE OF PRONUNCIATION LEXICONS FOR KEYWORD SEARCH IN LOW RESOURCE languageS

QUANTIFYING THE VALUE OF PRONUNCIATION LEXICONS FOR KEYWORD ...

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IEEE International Conference on Acoustics, speech, and Signal processing

作者： Guoguo Chen Sanjeev Khudanpur Daniel Povey Jan Trmal David Yarowsky Oguz Yilmaz Center for Language and Speech Processing and Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD 21218 USA

ISBN: (纸本)9781479903573

This paper quantifies the value of pronunciation lexicons in large vocabulary continuous speech recognition (LVCSR) systems that support keyword search (KWS) in low resource languages. Stateof-the-art LVCSR and KWS systems are developed for conversational telephone speech in Tagalog, and the baseline lexicon is augmented via three different grapheme-to-phoneme models that yield increasing coverage of a large Tagalog word-list. It is demonstrated that while the increased lexical coverage - or reduced out-of-vocabulary (OOV) rate - leads to only modest (ca 1%-4%) improvements in word error rate, the concomitant improvements in actual term weighted value are as much as 60%. It is also shown that incorporating the augmented lexicons into the LVCSR system before indexing speech is superior to using them post facto, e.g., for approximate phonetic matching of OOV keywords in pre-indexed lattices. These results underscore the disproportionate importance of automatic lexicon augmentation for KWS in morphologically rich languages, and advocate for using them early in the LVCSR stage.

关键词： speech Recognition Keyword Search Information Retrieval Morphology speech Synthesis Key Search speech synthesis information retrieval speech recognition Juvenile Spinal Muscular Atrophy Dictionaries as Topic Filipino language KEYWORD

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Open domain targeted sentiment

Open domain targeted sentiment

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2013 Conference on Empirical Methods in Natural language processing, EMNLP 2013

作者： Mitchell, Margaret Aguilar, Jacqueline Wilson, Theresa Van Durme, Benjamin Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781937284978

We propose a novel approach to sentiment analysis for a low resource setting. The intuition behind this work is that sentiment expressed towards an entity, targeted sentiment, may be viewed as a span of sentiment expressed across the entity. This representation allows us to model sentiment detection as a sequence tagging problem, jointly discovering people and organizations along with whether there is sentiment directed towards them. We compare performance in both Spanish and English on microblog data, using only a sentiment lexicon as an external resource. By leveraging linguistically-informed features within conditional random fields (CRFs) trained to minimize empirical risk, our best models in Spanish significantly outperform a strong baseline, and reach around 90% accuracy on the combined task of named entity recognition and sentiment prediction. Our models in English, trained on a much smaller dataset, are not yet statistically significant against their baselines. © 2013 Association for Computational Linguistics.

关键词： Sentiment analysis

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Broadly improving user classification via communication-based name and location clustering on twitter

Broadly improving user classification via communication-base...

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2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2013

作者： Bergsma, Shane Dredze, Mark Van Durme, Benjamin Wilson, Theresa Yarowsky, David Department of Computer Science Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781937284473

Hidden properties of social media users, such as their ethnicity, gender, and location, are often reflected in their observed attributes, such as their first and last names. Furthermore, users who communicate with each other often have similar hidden properties. We propose an algorithm that exploits these insights to cluster the observed attributes of hundreds of millions of Twitter users. Attributes such as user names are grouped together if users with those names communicate with other similar users. We separately cluster millions of unique first names, last names, and userprovided locations. The efficacy of these clusters is then evaluated on a diverse set of classification tasks that predict hidden users properties such as ethnicity, geographic location, gender, language, and race, using only profile names and locations when appropriate. Our readily-replicable approach and publiclyreleased clusters are shown to be remarkably effective and versatile, substantially outperforming state-of-The-Art approaches and human accuracy on each of the tasks studied. © 2013 Association for Computational Linguistics.

关键词： Location

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Using conceptual class attributes to characterize social media users

Using conceptual class attributes to characterize social med...

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51st Annual Meeting of the Association for Computational Linguistics, ACL 2013

作者： Bergsma, Shane Van Durme, Benjamin Department of Computer Science Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD 21218 United States

ISBN: (纸本)9781937284503

We describe a novel approach for automatically predicting the hidden demographic properties of social media users. Building on prior work in common-sense knowledge acquisition from third-person text, we first learn the distinguishing attributes of certain classes of people. For example, we learn that people in the Female class tend to have maiden names and engagement rings. We then show that this knowledge can be used in the analysis of first-person communication;knowledge of distinguishing attributes allows us to both classify users and to bootstrap new training examples. Our novel approach enables substantial improvements on the widelystudied task of user gender prediction, obtaining a 20% relative error reduction over the current state-of-the-art. © 2013 Association for Computational Linguistics.

关键词： Social networking (online)

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Separating fact from fear: Tracking flu infections on twitter

Separating fact from fear: Tracking flu infections on twitte...

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2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2013

作者： Lamb, Alex Paul, Michael J. Dredze, Mark Human Language Technology Center of Excellence Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781937284473

Twitter has been shown to be a fast and reliable method for disease surveillance of common illnesses like influenza. However, previous work has relied on simple content analysis, which conflates flu tweets that report infection with those that express concerned awareness of the flu. By discriminating these categories, as well as tweets about the authors versus about others, we demonstrate significant improvements on influenza surveillance using Twitter. © 2013 Association for Computational Linguistics.

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Separating Fact from Fear: Tracking Flu Infections on Twitter 2

Separating Fact from Fear: Tracking Flu Infections on Twitte...

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2nd Workshop on Computational Linguistics for Literature, CLfL 2013 at the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2013

作者： Lamb, Alex Paul, Michael J. Dredze, Mark Human Language Technology Center of Excellence Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781937284473

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