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EnglishtoKorean Transliteration using Multiple Unbounded Overlapping Phoneme Chunks
 In Proceedings of the 18th International Conference on Computational Linguistics
, 2000
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GENERALIZED WORD POSTERIOR PROBABILITY (GWPP) FOR MEASURING RELIABILITY OF RECOGNIZED WORDS
"... To measure the reliability of recognized words in an ASR, we propose a generalized word posterior probability (GWPP) as the sole confidence measure. This measure is computed efficiently via a word graph with the forwardbackward algorithm or directly with the generalized string likelihoods of Nbest ..."
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Cited by 9 (5 self)
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To measure the reliability of recognized words in an ASR, we propose a generalized word posterior probability (GWPP) as the sole confidence measure. This measure is computed efficiently via a word graph with the forwardbackward algorithm or directly with the generalized string likelihoods of Nbest strings from the recognizer. The GWPP is a modified word posterior probability where a word event, given all the acoustic observations of an utterance, is measured as a conditional probability. Time registration of the starting and ending frames of a hypothesized word is relaxed, similar to the BaumWelch model training algorithm, and acoustic and language model weights are optimally adjusted to accommodate instrumental but inaccurate modeling assumptions used in implementing those two models. When tested on the ATR Japanese BTEC speech database, the confidence error rates are significantly reduced as much as 25 % at various operating points. 1
Automatic Continuous Speech Recognition with Rapid Speaker Adaption for Human/Machine Interaction
, 1997
"... This thesis presents work in three main directions of the automatic speech recognition field. The work within two of these  dynamic decoding and hybrid HMM/ANN speech recognition  has resulted in a realtime speech recognition system, currently in use in the human/machine dialogue demonstra ..."
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Cited by 8 (0 self)
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This thesis presents work in three main directions of the automatic speech recognition field. The work within two of these  dynamic decoding and hybrid HMM/ANN speech recognition  has resulted in a realtime speech recognition system, currently in use in the human/machine dialogue demonstration system WAXHOLM, developed at the department. The third direction is fast unsupervised speaker adaptation, where "fast" refers to adaptation with a small amount of adaptation speech. The work in
ContextDependent Modeling in a SegmentBased Speech Recognition System
 S.M. thesis, MIT
, 1997
"... in partial ful llment of the requirements for the degree of ..."
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Cited by 6 (0 self)
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in partial ful llment of the requirements for the degree of
Finding the k Shortest Paths in Parallel
, 2000
"... . A concurrentread exclusivewrite PRAM algorithm is developed to find the k shortest paths between pairs of vertices in an edgeweighted directed graph. Repetitions of vertices along the paths are allowed. The algorithm computes an implicit representation of the k shortest paths to a given destina ..."
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Cited by 5 (0 self)
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. A concurrentread exclusivewrite PRAM algorithm is developed to find the k shortest paths between pairs of vertices in an edgeweighted directed graph. Repetitions of vertices along the paths are allowed. The algorithm computes an implicit representation of the k shortest paths to a given destination vertex from every vertex of a graph with n vertices and m edges, using O(m +nk log 2 k) work and O(log 3 k log # k + log n(log log k +log # n)) time, assuming that a shortest path tree rooted at the destination is precomputed. The paths themselves can be extracted from the implicit representation in O(log k +log n) time, and O(n log n+L) work, where L is the total length of the output. Key Words. Parallel graph algorithms, Data structures, Shortest paths. 1. Introduction. The problem of finding shortest paths in an edgeweighted graph is an important and wellstudied problem in computer science. The more general problem of computing the k shortest paths between vertices of...
Online handwriting recognition with constrained Nbest decoding
 Proceedings of the 13th International Conference on Pattern Recognition
, 1996
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Combined Optimisation Of Baseforms And Subword Models For An HNN Based Speech Recogniser
 in Proc. The 4th Int. Symposium on Signal Processing and its Applications (ISSPA), (Gold
, 1996
"... In this paper a framework for combined optimisation of baseforms and subword models for a speech recogniser is proposed. Given a set of subword Hidden Markov Models (HMMs) and a set of utterances of a specific word, the modified treetrellis algorithm and the BaumWelch reestimation procedure is use ..."
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Cited by 4 (4 self)
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In this paper a framework for combined optimisation of baseforms and subword models for a speech recogniser is proposed. Given a set of subword Hidden Markov Models (HMMs) and a set of utterances of a specific word, the modified treetrellis algorithm and the BaumWelch reestimation procedure is used iteratively to achieve a combined optimisation of baseforms and subword models. The DARPA Resource Management (RM) database was used to evaluate the combined optimisation scheme. The proposed method resulted in a monotonic increase in the likelihood score of both test and training data. When compared to the initial lexicon derived from the DARPA RMdistribution and a set of initial HMMs, a 13% reduction in word error rate is achieved at best. 1. INTRODUCTION Modern large vocabulary speech recognisers employ subwords as the basic modelling units. This implies that in order to recognise words (or sentences), a lexicon which defines the composition of the vocabulary words in terms of the b...
Twostage continuous speech recognition using featurebased models: A premliminary study
 In Proc. IEEE Automatic Speech Recognition and Understanding Workshop
, 2003
"... In recent research, we have demonstrated that linguistic features can be used to improve speech recognition for an isolated vocabulary recognition task. This paper addresses two important new research problems in our attempts to build a twostage speech recognition system using linguistic features. ..."
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Cited by 3 (2 self)
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In recent research, we have demonstrated that linguistic features can be used to improve speech recognition for an isolated vocabulary recognition task. This paper addresses two important new research problems in our attempts to build a twostage speech recognition system using linguistic features. First, through a controlled study we show that our knowledgedriven feature sets perform competitively when compared with similar classes discovered by datadriven approaches. Secondly, we show that the cohort idea can be effectively generalized to continuous speech. Improved recognition results are achieved using this twostage framework on multiple speech recognition experiments, on conversational telephone quality speech. 1.
Realtime word confidence scoring using local posterior probabilities on tree trellis search
 In Proc. ICASSP,volume 1
, 2004
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NBest Breadth Search For Large Vocabulary Continuous Speech Recognition Using A Long Span Language Model
, 1998
"... In large vocabulary continuous speech recognition, high level linguistic knowledge can enhance performance. However, integration of high level linguistic knowledge and complex acoustic models under an efficient search scheme is still an open question. In this paper, we propose the nbest breadth sea ..."
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Cited by 3 (3 self)
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In large vocabulary continuous speech recognition, high level linguistic knowledge can enhance performance. However, integration of high level linguistic knowledge and complex acoustic models under an efficient search scheme is still an open question. In this paper, we propose the nbest breadth search algorithm under the framework of a state space search. The nbest breadth search is a combination of the best first search and the breadth first search, and it efficiently accommodates the long span language models and complex acoustic models. Our pilot experiment shows that the proposed algorithm decreases execution time with little effect on performance. 136th Meeting of Acoustical Society of America 2 Contents 1 INTRODUCTION 3 2 REVIEW OF DECODING ALGORITHMS 4 3 NBEST BREADTH SEARCH 5 4 IMPLEMENTATION ISSUES 7 5 EXPERIMENTAL RESULTS 8 6 CONCLUSIONS 9 7 ACKNOWLEDGMENT 136th Meeting of Acoustical Society of America 3 1 INTRODUCTION In the statistical approach, speech recognition ...