Search Results - "Wade Shen"

Characterizing Phonetic Transformations and Acoustic Differences Across English Dialects by Chen, Nancy F., Tam, Sharon W., Wade Shen, Campbell, Joseph P.

“…In this work, we propose a framework that automatically discovers dialect-specific phonetic rules. These rules characterize when certain phonetic or acoustic…”
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Speaker Verification Using Support Vector Machines and High-Level Features by Campbell, W.M., Campbell, J.P., Gleason, T.P., Reynolds, D.A., Wade Shen

“…High-level characteristics such as word usage, pronunciation, phonotactics, prosody, etc., have seen a resurgence for automatic speaker recognition over the…”
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Improved GMM-based language recognition using constrained MLLR transforms by Wade Shen, Reynolds, D.

“…In this paper we describe the application of a feature-space transform based on constrained maximum likelihood linear regression for unsupervised compensation…”
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Analysis of factors affecting system performance in the ASpIRE challenge by Melot, Jennifer, Malyska, Nicolas, Ray, Jessica, Wade Shen

“…This paper presents an analysis of factors affecting system performance in the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge. In…”
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Human error rates for speaker recognition by Shen, Wade, Campbell, Joseph P., Schwartz, Reva

“…It is commonly assumed that speaker identification by human listeners is an innate skill under certain conditions. As such, human listening tests have served…”
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When to punt on speaker comparison? by Schwartz, Reva, Campbell, Joseph P., Shen, Wade

“…In forensic speaker comparison, it is crucial to decide when completion of the examination may not possible (punt). We explore the factors that make speaker…”
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A linguistically-informative approach to dialect recognition using dialect-specific context-dependent phonetic models by Chen, Nancy F., Shen, Wade, Campbell, Joseph P.

“…In this work, we explore automatic approaches to learn dialect discriminating pronunciation patterns and use these patterns to automatically recognize…”
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Advanced Language Recognition using Cepstra and Phonotactics: MITLL System Performance on the NIST 2005 Language Recognition Evaluation by Campbell, W., Gleason, T., Navratil, J., Reynolds, D., Shen, W., Singer, E., Torres-Carrasquillo, P.

“…This paper presents a description of the MIT Lincoln Laboratory submissions to the 2005 NIST Language Recognition Evaluation (LRE05). As was true in 2003, the…”
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Efficient Speech Translation Through Confusion Network Decoding by Bertoldi, N., Zens, R., Federico, M., Shen, W.

“…This paper describes advances in the use of confusion networks as interface between automatic speech recognition and machine translation. In particular, it…”
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Assessing the speaker recognition performance of naive listeners using mechanical turk by Shen, Wade, Campbell, Joseph, Straub, Derek, Schwartz, Reva

“…In this paper we attempt to quantify the ability of naive listeners to perform speaker recognition in the context of the NIST evaluation task. We describe our…”
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A linguistically-informative approach to dialect recognition using dialect-discriminating context-dependent phonetic models by Chen, Nancy F, Shen, Wade, Campbell, Joseph P

“…We propose supervised and unsupervised learning algorithms to extract dialect discriminating phonetic rules and use these rules to adapt biphones to identify…”
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Query-by-example spoken term detection using phonetic posteriorgram templates by Hazen, T.J., Shen, W., White, C.

“…This paper examines a query-by-example approach to spoken term detection in audio files. The approach is designed for low-resource situations in which limited…”
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Informative dialect recognition using context-dependent pronunciation modeling by Chen, Nancy F., Shen, Wade, Campbell, Joseph P., Torres-Carrasquillo, Pedro A.

“…We propose an informative dialect recognition system that learns phonetic transformation rules, and uses them to identify dialects. A hidden Markov model is…”
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Speaker verification using support vector machines and high-level features : Speaker and language recognition by CAMPBELL, William M, CAMPBELL, Joseph P, GLEASON, Terry P, REYNOLDS, Douglas A, WADE SHEN

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USSS-MITLL 2010 human assisted speaker recognition by Schwartz, Reva, Campbell, Joseph P., Shen, Wade, Sturim, Douglas E., Campbell, William M., Richardson, Fred S., Dunn, Robert B., Granville, Robert

“…The United States Secret Service (USSS) teamed with MIT Lincoln Laboratory (MIT/LL) in the US National Institute of Standards and Technology's 2010 Speaker…”
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Comparing a High and Low-Level Deep Neural Network Implementation for Automatic Speech Recognition by Ray, Jessica, Thompson, Brian, Shen, Wade

“…The use of deep neural networks (DNNs) has improved performance in several fields including computer vision, natural language processing, and automatic speech…”
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Measuring human readability of machine generated text: three case studies in speech recognition and machine translation by Jones, D., Gibson, E., Shen, W., Granoien, N., Herzog, M., Reynolds, D., Weinstein, C.

“…We present highlights from three experiments that test the readability of current state-of-the art system output from: (1) an automated English speech-to-text…”
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Low-resource speech translation of Urdu to English using semi-supervised part-of-speech tagging and transliteration by Ryan Aminzadeh, A., Shen, W.

“…This paper describes the construction of ASR and MT systems for translation of speech from Urdu into English. As both Urdu pronunciation lexicons and…”
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Experiments with Lattice-based PPRLM Language Identification by Shen, W., Campbell, W., Gleason, T., Reynolds, D., Singer, E.

“…In this paper we describe experiments conducted during the development of a lattice-based PPRLM language identification system as part of the NIST 2005…”
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Experimental facility for measuring the impact of environmental noise and speaker variation on speech-to-speech translation devices by Jones, D.A., Jairam, A., Shen, W., Gatewood, P., Tardelli, J., Emonts, M.

“…We describe the construction and use of a laboratory facility for testing the performance of speech-to-speech translation devices. Approximately 1500 English…”
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