# zhangle.bib

@INPROCEEDINGS{anderson2010,
AUTHOR = {Paul Anderson and Le Zhang},
BOOKTITLE = {Proceedings of the Large Installations Systems Administration (LISA) Conference},
MONTH = {November},
ORGANIZATION = {Usenix Association},
TITLE = {Fast and Secure Laptop Backups with Encrypted De-duplication},
URL = {http://homepages.inf.ed.ac.uk/dcspaul/publications/lisa2010.pdf},
YEAR = {2010}
}


@INPROCEEDINGS{Garner2009a,
AUTHOR = {Garner, Philip N. and Dines, John and Hain, Thomas
and El Hannani, Asmaa and Karafi{\'a}t, Martin and
Korchagin, Danil and Lincoln, Mike and Wan, Vincent
and Zhang, Le},
TITLE = {Real-Time {ASR} from Meetings},
BOOKTITLE = {Proceedings of Interspeech},
YEAR = 2009,
MONTH = {September},
}


@PHDTHESIS{zhang-thesis2009,
AUTHOR = {Le Zhang},
TITLE = {Modelling Speech Dynamics with Trajectory-{HMM}s},
SCHOOL = {School of Informatics, University of Edinburgh},
YEAR = 2009,
CATEGORIES = {speech recognition, speech synthesis, MOCHA, trajectory HMM},
MONTH = {January},
ABSTRACT = {
The conditional independence assumption imposed
by the hidden Markov models (HMMs) makes it difficult to model temporal
correlation patterns in human speech. Traditionally, this limitation
is circumvented by appending the first and second-order regression
coefficients to the observation feature vectors. Although this leads
to improved performance in recognition tasks, we argue that a straightforward
use of dynamic features in HMMs will result in an inferior model,
due to the incorrect handling of dynamic constraints. In this thesis
I will show that an HMM can be transformed into a Trajectory-HMM capable
of generating smoothed output mean trajectories, by performing a per-utterance
normalisation. The resulting model can be trained by either maximising
model log-likelihood or minimising mean generation errors on the training
data. To combat the exponential growth of paths in searching, the
idea of delayed path merging is proposed and a new time-synchronous
decoding algorithm built on the concept of token-passing is designed
for use in the recognition task. The Trajectory-HMM brings a new way
of sharing knowledge between speech recognition and synthesis components,
by tackling both problems in a coherent statistical framework. I evaluated
the Trajectory-HMM on two different speech tasks using the speaker-dependent
MOCHA-TIMIT database. First as a generative model to recover articulatory
features from speech signal, where the Trajectory-HMM was used in
a complementary way to the conventional HMM modelling techniques,
within a joint Acoustic-Articulatory framework. Experiments indicate
that the jointly trained acoustic-articulatory models are more accurate
(having a lower Root Mean Square error) than the separately trained
ones, and that Trajectory-HMM training results in greater accuracy
compared with conventional Baum-Welch parameter updating. In addition,
the Root Mean Square (RMS) training objective proves to be consistently
better than the Maximum Likelihood objective. However, experiment
of the phone recognition task shows that the MLE trained Trajectory-HMM,
while retaining attractive properties of being a proper generative
model, tends to favour over-smoothed trajectories among competing
hypothesises, and does not perform better than a conventional HMM.
We use this to build an argument that models giving a better fit on
training data may suffer a reduction of discrimination by being too
faithful to the training data. Finally, experiments on using triphone
models show that increasing modelling detail is an effective way to
leverage modelling performance with little added complexity in training.
},
}


@ARTICLE{zhang-spl2008,
AUTHOR = {Le Zhang and Steve Renals},
TITLE = {Acoustic-Articulatory Modelling with the Trajectory {HMM}},
JOURNAL = {IEEE Signal Processing Letters},
YEAR = 2008,
VOLUME = 15,
PAGES = {245-248},
CATEGORIES = {articulatory inversion},
ABSTRACT = {
In this letter, we introduce an hidden Markov model (HMM)-based inversion system to recovery articulatory movements from speech acoustics. Trajectory HMMs are used as generative models for modelling articulatory data. Experiments on the MOCHA-TIMIT corpus indicate that the jointly trained acoustic-articulatory models are more accurate (lower RMS error) than the separately trained ones, and that trajectory HMM training results in greater accuracy compared with conventional maximum likelihood HMM training. Moreover, the system has the ability to synthesize articulatory movements directly from a textual representation.
}
}


@INPROCEEDINGS{zhang-icslp2006,
AUTHOR = {Le Zhang and Steve Renals},
TITLE = {Phone Recognition Analysis for Trajectory {HMM}},
BOOKTITLE = {Proc. Interspeech 2006},
YEAR = 2006,
MONTH = {September},
ABSTRACT = {
The trajectory {HMM} has been shown to be useful for model-based speech
synthesis where a smoothed trajectory is generated using temporal
constraints imposed by dynamic features. To evaluate the performance of such
model on an ASR task, we present a trajectory decoder based on tree search
with delayed path merging. Experiment on a speaker-dependent phone
recognition task using the MOCHA-TIMIT database shows that the MLE-trained
trajectory model, while retaining attractive properties of being a proper
generative model, tends to favour over-smoothed trajectory among competing
hypothesises, and does not perform better than a conventional {HMM}. We use
this to build an argument that models giving better fit on training data may
suffer a reduction of discrimination by being too faithful to training data.
This partially explains why alternative acoustic models that try to
explicitly model temporal constraints do not achieve significant
improvements in ASR.
},
CATEGORIES = {asr}
}


@ARTICLE{zhang-talip2004,
AUTHOR = {Le Zhang and Jingbo Zhu and Tianshun Yao},
TITLE = {An Evaluation of Statistical Spam Filtering
Techinques},
JOURNAL = {ACM Transactions on Asian Language Information
Processing (TALIP)},
YEAR = 2004,
VOLUME = 3,
NUMBER = 4,
PAGES = {243-269},
MONTH = {December},
ABSTRACT = {
This paper evaluates five supervised learning methods in the context of
statistical spam filtering. We study the impact of different feature
pruning methods and feature set sizes on each learner's performance using
cost-sensitive measures. It is observed that the significance of feature
selection varies greatly from classifier to classifier. In particular, we
found Support Vector Machine, AdaBoost and Maximum Entropy Model are top
performers in this evaluation, sharing similar characteristics: not
sensitive to feature selection strategy, easily scalable to very high
feature dimension and good performances across different datasets. In
contrast, Naive Bayes, a commonly used classifier in spam filtering, is
found to be sensitive to feature selection methods on small feature set,
and fail to function well in scenarios where false positives are penalized
heavily. The experiments also suggest that aggressive feature pruning
should be avoided when building filters to be used in applications where
legitimate mails are assigned a cost much higher than spams (such as
$\lambda=999$), so as to maintain a better-than-baseline
performance. An interesting finding is the effect of mail headers on spam
filtering, which is often ignored in previous studies. Experiments show
that classifiers using features from message header alone can achieve
comparable or better performance than filters utilizing body features
only. This suggests that message headers can be reliable and powerfully
discriminative feature sources for spam filtering.},
ZH1CORPUS = {http://homepages.inf.ed.ac.uk/s0450736/spam/zh1.tar.bz2},
PS = {http://homepages.inf.ed.ac.uk/s0450736/paper/2004-spameval.ps.gz},
PDF = {http://homepages.inf.ed.ac.uk/s0450736/paper/2004-spameval.pdf}
}


@INPROCEEDINGS{Lv-ijcnlp2004,
AUTHOR = {Xueqiang L\"{U} and Le Zhang and Junfeng Hu},
TITLE = {Statistical Substring Reduction in Linear Time},
BOOKTITLE = {Proceeding of the 1st International Joint Conference
on Natural Language Processing (IJCNLP-04)},
YEAR = 2004,
ADDRESS = {Sanya, Hainan island, China},
MONTH = {March},
ABSTRACT = {We study the problem of efficiently removing equal
frequency ngram substrings from an ngram set,
formally called Statistical Substring Reduction
(SSR). SSR is a useful operation in corpus based
multi-word unit research and new word identification
task of oriental language processing. We present a
new SSR algorithm that has linear time ($O(n)$), and
prove its equivalence with the traditional $O(n^2)$
algorithm. In particular, using experimental results
from several corpora with different sizes, we show
that it is possible to achieve performance close to
that theoretically predicated for this task. Even in
a small corpus the new algorithm is several orders
of magnitude faster than the $O(n^2)$ one. These
results show that our algorithm is reliable and
efficient, and is therefore an appropriate choice
for large scale corpus processing.},
SOFTWARE = {http://homepages.inf.ed.ac.uk/s0450736/ngram.html},
}


@INPROCEEDINGS{zhang-iccpol2003,
AUTHOR = {Le Zhang and Tianshun Yao},
TITLE = {Filtering Junk Mail with a Maximum Entropy Model},
BOOKTITLE = {Proceeding of 20th International Conference on
Computer Processing of Oriental Languages
(ICCPOL03)},
YEAR = 2003,
PAGES = {446-453},
ABSTRACT = {
The task of junk mail filtering is to rule out unsolicited bulk e-mail (junk)
automatically from a user's mail stream. Two classes of methods have been
shown to be useful for classifying e-mail messages. The rule based method
uses a set of heuristic rules to classify e-mail messages while the
statistical based approach models the difference of messages
statistically, usually under a machine learning framework. Generally
speaking, the statistical based methods are found to outperform the rule
based method, yet we found, by combining different kinds of evidence used
in the two approaches into a single statistical model, further improvement
can be obtained. We present such a hybrid approach, utilizing a Maximum
Entropy Model, and show how to use it in a junk mail filtering task. In
particular, we present an extensive experimental comparison of our
approach with a Naive Bayes classifier, a widely used classifier in e-mail
filtering task, and show that this approach performs comparable or better
than Naive Bayes method.
},
PS = {http://homepages.inf.ed.ac.uk/s0450736/paper/junk.ps.gz},
PDF = {http://homepages.inf.ed.ac.uk/s0450736/paper/junk.pdf},
SLIDE = {http://homepages.inf.ed.ac.uk/s0450736/paper/junk-slide.pdf},
SOFTWARE = {http://homepages.inf.ed.ac.uk/s0450736/maxent_toolkit.html}
}


@INPROCEEDINGS{zhang-iccpol2003b,
AUTHOR = {Le Zhang and Xueqiang L\"{U} and Yanna Shen and
Tianshun Yao},
TITLE = {A Statistical Approach to Extract Chinese Chunk
Candidates from Large Corpora},
BOOKTITLE = {Proceeding of 20th International Conference on
Computer Processing of Oriental Languages
(ICCPOL03)},
YEAR = 2003,
PAGES = {109-117},
ABSTRACT = {
The extraction of Chunk candidates from real corpora is one of the fundamental
tasks of building example-based machine translation model. This paper
presents a statistical approach to extract Chinese chunk candidates from
large monolingual corpora. The first step is to extract large N-grams (up
to 20-gram) from raw corpus. Then two newly proposed Fast
Statistical Substring Reduction (FSSR) algorithms can be applied to the
initial N-gram set to remove some unnecessary N-grams using their
frequency information. The two algorithms are efficient (both have a time
complexity of $O(n)$) and can effectively reduce the size of
N-gram set up to 50\%. Finally, mutual information is used to obtain chunk
candidates from reduced N-gram set. Perhaps the biggest contribution of
this paper is that it is the first time to apply Fast Statistical
Substring Reduction algorithm to large corpora and demonstrate the
effectiveness and efficiency of this algorithm which, in our hope, will
shed new light on large scale corpus oriented research. Experiments on
three corpora with different sizes show that this method can extract chunk
candidates from corpora of giga bytes efficiently under current
computational power. We get an extraction accuracy of 86.3\% from People
Daily 2000 news corpus.
},
PS = {http://homepages.inf.ed.ac.uk/s0450736/paper/extract_chunk.ps.gz},
PDF = { http://homepages.inf.ed.ac.uk/s0450736/paper/extract_chunk.pdf},
SLIDE = {http://homepages.inf.ed.ac.uk/s0450736/paper/extract_chunk_slide.pdf},
SOFTWARE = {http://homepages.inf.ed.ac.uk/s0450736/ngram.html}
}


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