Walter D. Andrews

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• This work is sponsored by the Department of Defense under Air Force Contract F19628-00-C-0002.and the CLSP/JHU workshop was supported by NSF and DoD fudning. Opinions, interpretations, conclusions and recommendations are those of the authors and are not necessarily endorsed by the United States Government + The authors gratefully acknowledge the CLSP(More)
This paper describes improvements to an innovative high-performance speaker recognition system. Recent experiments showed that with sufficient training data phone strings from multiple languages are exceptional features for speaker recognition. The prototype phonetic speaker recognition system used phone sequences from six languages to produce an equal(More)
Recent studies show that phonetic sequences from multiple languages can provide effective features for speaker recognition. So far, only pronunciation dynamics in the time dimension, i.e., n-gram modeling on each of the phone sequences, have been examined. In the JHU 2002 Summer Workshop, we explored modeling the statistical pronunciation dynamics across(More)
Recent work in phonetic speaker recognition has shown that modeling phone sequences using n-grams is a viable and effective approach to speaker recognition, primarily aiming at capturing speaker-dependent pronunciation and also word usage. This paper describes a method involving binary-tree-structured statistical models for extending the phonetic context(More)
This paper describes a text-independent speaker recognition system that achieves an equal error rate of less than 1% by combining phonetic, idiolect, and acoustic features. The phonetic system is a novel language-independent speakerrecognition system based on differences among speakers in dynamic realization of phonetic features (i.e., pronunciation),(More)
This paper describes a newly realized highperformance speaker recognition system and examines methods for its improvement. Innovative experiments early this year showed that phone strings are exceptional features for speaker recognition. The original system produced equal error rates less than 11.5% on Switchboard-I audio files. Subsequent research(More)
  • Alex, Synthèse L, +43 authors D D Palmer Vii Foreword
  • 2001
The NATO Native and Non-Native (N4) corpus has been developed by the NATO research group on Speech and Language Technology, in order to provide a militaryoriented database for multilingual and non-native speech processing studies. Speech data has been recorded in the Naval transmission training centers of four countries (Germany, The Netherlands, UK and(More)
The technique known as bootstrapping or resampling has been used effectively in the field of statistics to obtain good estimates of statistics from only a small set of observations. In this paper we explore the use of this powerful technique to aid in improving the performance of a GMM-UBM text-independent speaker recognition system. We apply the bootstrap(More)
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