phoneme recognition research paper-12
A novel model for phoneme recognition using phonetically derived features
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N Harte, S Vaseghi ,Eusipco: European signal processing , 1998 ,eurasip.org
ABSTRACT This paper presents work on the use of segmental modelling and phonetic
features for phoneme based speech recognition. The motivation for the work is to lessen the
e ects of the IID assumption in HMM based recognition. The use of phonetic features
A Hierarchical Broad-Class Classification To Enhance Phoneme Recognition
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C Lopes ,Proc. EUSIPCO, 2009 ,eusipco2009.org
ABSTRACT In this paper a hierarchical classification of different levels of phonetic
information is proposed in order to improve phone recognition. In this paradigm several
intermediate classifiers give posterior probability predictions for broad phonetic classes,
Analysis of Confusion Matrix to Combine Evidence for Phoneme Recognition
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SRM Prasanna, B Yegnanarayana , Report (IDIAP-RR- , 2007 ,infoscience.epfl.ch
Abstract. In this work we analyze and combine evidences from different classifiers for
phoneme recognition using information from the confusion matrices. Speech signals are
processed to extract the Perceptual Linear Prediction (PLP) and Multi-RASTA (MRASTA)
Minimum phoneme error based heteroscedastic linear discriminant analysis for speechrecognition
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B Zhang ,Proceedings of ICASSP, 2005 ,anoncvs.plt-scheme.org
ABSTRACT In this paper we introduce a discriminative feature analysis method that seeks to
minimize phoneme errors in latticebased training frameworks. This technique, referred to as
Minimum Phoneme Error Heteroscedastic Linear Discriminant Analysis (MPE-HLDA), is
Using English phoneme models for Chinese speech recognition
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MAC Yuen ,International Symposium on Chinese Spoken , 1998 ,colips.org
ABSTRACT To build a speech recognizer, database design, collection and transcription is
the most time consuming and tedious job. This paper proposes some fast and easy methods
to use English phoneme models for Mandarin and Cantonese speech recognition with
Improved phoneme-history-dependent search method for large-vocabulary continuous-speechrecognition
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T Hori, Y Noda ,IEICE TRANSACTIONS ON , 2003 ,kecl.ntt.co.jp
SUMMARY This paper presents an improved phonemehistory-dependent (PHD) search
algorithm. This method is an optimum algorithm under the assumption that the starting time
of a recognized word depends on only a few preceding phonemes (phoneme history). The
Synthetic phoneme prototypes and dynamic voice source adaptation in speech recognition
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M Blomberg ,QPSR, 1993 ,speech.kth.se
ABSTRACT A speech production oriented technique for generating reference spectral data
for speech recognition is presented as an alternative to training to natural speech. The
potentials of this approach are discussed. In the presented recognition system, the
Diagnostics of speech recognition using classification phoneme diagnostic trees
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Computational Intelligence, 2006 ,ui.savba.sk
ABSTRACT More than three decades of speech recognition research resulted in a very
sophisticated statistical framework. However, less attention was still devoted to diagnostics
of speech recognition; most previous research report on results in terms of ever-lower
The Interplay of Signal Analysis and Phoneme Modelling Techniques on Phoneme Recognition
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HA Engelbrecht ,Telecommunications and Digital Signal ,prasa.up.ac.za
Abstract This paper critically evaluates the currently available signal analysis techniques
and the modelling of phonemes with different probability density functions, as applied to
isolated, contextindependent, phoneme recognition. This is done to determine
Multiple source phoneme recognition aided by articulatory features
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M Kane ,Modern Approaches in Applied Intelligence, 2011 ,Springer
This paper presents an experiment in speech recognition whereby multiple phoneme
recognisers are applied to the same utterance. When these recognisers agree on an
hypothesis for the same time interval, that hypothesis is assumed to be correct. When they
Arabic phoneme recognition using neural networks
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M El-Obaid, A Al-Nassiri ,Proceedings of the 5th WSEAS , 2006 ,wseas.us
Abstract:-The main theme of this paper is the recognition of isolated Arabic speech
phonemes using artificial neural networks, as most of the researches on speech recognition
(SR) are based on Hidden Markov Models (HMM). The technique in this paper can be
NEURAL NETWORK WITH APPLICATION TO REAL TIME PHONEME RECOGNITION
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ES Kim ,2001 ,ese.upenn.edu
ABSTRACT In hopes of improving real-time speech recognition, a biologically based
phoneme recognition algorithm was implemented on the NP-4 neural computer. The NP-4
neural computer, which contains programmable interconnects, neurons, synapses, and
Sparse Multilayer Perceptron for Phoneme Recognition
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IEEE Transactions on Audio Speech , 2012 ,sites.google.com
Abstract—This paper introduces the sparse multilayer perceptron (SMLP) which jointly
learns a sparse feature representation and nonlinear classifier boundaries to optimally
discriminate multiple output classes. SMLP learns the transformation from the inputs to the
Feature Reduction with Linear Discriminant Analysis and its Performance on Phoneme Recognition
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S Geirhofer , Speech and Language Engineering, University of , 2004 ,isle.illinois.edu
In the previous section various ways of representing speech have been addressed.
Specifically, shortterm properties of speech, processed roughly every 10ms and stored in
vector format turned out to be a good representation. It has been shown how to arrive at
Syllable–phoneme based continuous speech recognition
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S Laurinciukaite ,Electronics and Electrical Engineering.–Kaunas: , 2006 ,ktu.lt
State-of-the-art speech recognition systems are based on the use of the sub-word units, ie
words in such systems are modelled as sequences of previously defined phonemes,
syllables or from acoustic signal derived units. We can assign all listed units to one of the
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