speech recognition 3




HIGH levels of speech recognition have been achieved with a new sound processing strategy for multielectrode cochlear implants. A cochlear implant system consists of one or more implanted elec-trodes for direct electrical activation of the auditory nerve, an externalAutomatic Speech Recognition (ASR), which is aimed to enable natural human machine interaction, has been an intensive research area for decades. Many core technologies, such as Gaussian mixture models (GMMs), hidden Markov models (HMMs), mel-frequency

Towards end-to-end speech recognition with recurrent neural networks
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This paper presents a speech recognition system that directly transcribes audio data with text, without requiring an intermediate phonetic representation. The system is based on a combination of the deep bidirectional LSTM recurrent neural network architecture and the

Audio visual speech recognition
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We have made significant progress in automatic speech recognition ASR for well-defined applications like dictation and medium vocabulary transaction processing tasks in relatively controlled environments. However, for ASR to approach human levels of performance and

Speech recognition in noisy environments
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Figure 2-1.: Block diagram of SPHINX-II. 18 Figure 2-2.: Block diagram of SPHINX-IIs front end. 21 Figure 2-3.: The topology of the phonetic HMM used in the SPHINX-II system 23 Figure 3-1: Outline of the algorithms for environment

A review on speech recognition technique
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The Speech is most prominent primary mode of Communication among of human being. The communication among human computer interaction is called human computer interface. Speech has potential of being important mode of interaction with computer. This paper gives

Discriminative training for large vocabulary speech recognition
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This thesis investigates the use of discriminative criteria for training HMM parameters for speech recognition , in particular the Maximum Mutual Information (MMI) criterion and a new criterion called Minimum Phone Error (MPE). Investigations are conducted into the practical

Constrained iterative speech enhancement with application to speech recognition
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In this paper, an improved form of iterative speech enhancement for single channel inputs is formulated. The basis of the procedure is sequential maximum a posteriori estimation of the speech waveform and its all-pole parameters as originally formulated by Lim and

Speech recognition with dynamic Bayesian networks
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Abstract Dynamic Bayesian networks (DBNs) are a useful tool for representing complex stochastic processes. Recent developments in inference and learning in DBNs allow their use in real-world applications. In this paper, we apply DBNs to the problem of speech

Audio-visual automatic speech recognition : An overview
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We have made significant progress in automatic speech recognition (ASR) for well-defined applications like dictation and medium vocabulary transaction processing tasks in relatively controlled environments. However, ASR performance has yet to reach the level required for

Linear discriminant analysis for improved large vocabulary continuous speech recognition
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ABSTRACT interaction of Linear Discriminant Analyand a modeling approach using continuous mixture density HMMs is studied experimentally. The largest improvements in speech recognition accuracy could be obtained when the classes for the LDA transform

Speech recognition using neural networks
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This thesis examines how artificial neural networks can benefit a large vocabulary, speaker independent, continuous speech recognition system. Currently, most speech recognition systems are based on hidden Markov models (HMMs), a statistical framework that supports

Speech recognition using SVMs
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An important issue in applying SVMs to speech recognition is the ability to classify variable length sequences. This paper presents extensions to a standard scheme for handling this variable length data, the Fisher score. A more useful mapping is introduced based on the

Large-vocabulary speech recognition under adverse acoustic environments
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We report our recent work on noise-robust large-vocabulary speech recognition . Three key innovations are developed and evaluated in this work: 1) a new model learning paradigm that comprises a noise-insertion process followed by noise reduction; 2) a noise adaptive

Robust entropy-based endpoint detection for speech recognition in noisy environments
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This paper presents an entropy-based algorithm for accurate and robust endpoint detection for speech recognition under noisy environments. Instead of using the conventional energy- based features, the spectral entropy is developed to identify the speech segments

Visual speech recognition with stochastic networks
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This paper presents ongoing work on a speaker independent visual speech recognition system. The work presented here builds on previous research efforts in this area and explores the potential use of simple hidden Markov models for limited vocabulary, speaker

Automatic speech recognition a brief history of the technology development
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Designing a machine that mimics human behavior, particularly the capability of speaking naturally and responding properly to spoken language, has intrigued engineers and scientists for centuries. Since the 1930s, when Homer Dudley of Bell Laboratories proposed

The use of context in large vocabulary speech recognition
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In recent years, considerable progress has been made in the eld of continuous speech recognition where the predominant technology is based on hidden Markov models (HMMs). HMMs represent sequences of time varying speech spectra using probabilistic functions of

Model-based techniques for noise robust speech recognition
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Summ ar y This thesis details the development of a model-based noise compensation techniq ue, Parallel Model Combination (PMC). The aim of PMC is to alter the parameters of a set of H idden Markov Model (H MM) based acoustic models, so that they re fl ect speech

Acoustical and environmental robustness in automatic speech recognition
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This dissertation describes a number of algorithms developed to increase the robustness of automatic speech recognition systems with respect to changes in the environment. These algorithms attempt to improve the recognition accuracy of speech recognition systems when




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