speech recognition 2019


Speech recognition is the inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers.

Improving Children Speech Recognition through Feature Learning from Raw Speech Signal
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Children speech recognition based on short-term spectral features is a challenging task. One of the reasons is that children speech has high fundamental frequency that is Comparable to formant frequency values. Furthermore, as children grow, their vocal

Speech Recognition Systems A Comprehensive Study Of Concepts And Mechanism
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ABSTRACT Speech Recognition Systems now-a-days use many interdisciplinary Technologies ranging from Pattern Recognition, Signal Processing, Natural Language Processing implementing to unified statistical framework. Such systems find a wide area of

Speech Recognition in High Noise Environment
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Most of the existing speech recognition products require the speech to be extracted in a mall noise or no noise environment. It aims to obtain the correct speech information. However, such speech recognition cannot be used in our normal environment. For example

Boosting Neuro Evolutionary Techniques for Speech Recognition
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For long many years, various speech signal processing techniques have been Experimented and optimized using expectation maximization, gradient descent optimization Or their variations across end-to-end speech feature extraction and recognition scheme, but

Segment-level training of ANNs based on acoustic confidence measures for hybrid HMM/ANN Speech Recognition
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We show that confidence measures estimated from local posterior probabilities can serve as Objective functions for training ANNs in hybrid HMM based speech recognition systems. This Leads to a segment-level training paradigm that overcomes the limitation of frame-level

PERFORMANCE COMPARISON OF ROBUST SPEECH RECOGNITION USING DIFFERENT FEATURE EXTRACTION TECHNIQUES
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the principal target of talk affirmation zone is to make techniques and structures for talk Commitment to machine. Talk is the basic techniques for correspondence between Individuals. For reasons going from inventive enthusiasm about the segments for

Triggered Attention for End-to-End Speech Recognition
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new system architecture for end-to-end automatic speech recognition (ASR) is proposed hat combines the alignment capabilities of the connectionist temporal classification (CTC) Approach and the modeling strength of the attention mechanism. The proposed system

Recurrent Poisson Process Unit for Speech Recognition
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ver the past few years, there has been a resurgence of interest in using recurrent neural Network-hidden Markov model (RNN-HMM) for automatic speech recognition (ASR). Some odern recurrent network models, such as long shortterm memory (LSTM) and simple

Unifying Speech Recognition and Generation with Machine Speech Chain
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Human speech chain was first introduced by. , describes the basic mechanism Involved in speech communication when a spoken message travels from the speakers mind o the listeners mind (Fig. 1). It consists of a speech production mechanism in which the

Distilling Knowledge from Wikipedia for Augmented Speech Recognition
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Accessibility is a long-neglected frontier in educational institutions. Though regulations have een ratified in the Americans with Disabilities Act which require institutions to Accommodate less able members by distributing their media in multiple formats, almost no

On the Effect of the Implementation of Human Auditory Systems on Q-Log-Based Features for Robustness of Speech Recognition Against Noise
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In the past decades, automatic speech recognition (ASR) technologies have been explored n many studies. However, their practical applications are still very limited due to their reliable performances in real environments where background noise, reverberation, and

Speech Recognition using AANN
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Human beings communicate in either of the two ways, through speech or by writing. The Most common mode of communication is speech, because it doesnt take much time to Transmit information from one source to receptor. This paper describes a technique that uses

Robust feature extraction for visual speech and speaker recognition
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In this we have studied and compared many visual features for Speech Recognition and many Audio features for speaker recognition. The desire goal information only is called as visual speech Recognition andSpeaker recognition is nothing but identifying speaker by

Hindi Speech Vowel Recognition Using Hidden Markov Model}}
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ABSTRACT Vowel recognition is an important step for developing speech recognition system home based speech recognition are widely used to overcome vocabulary constraint in Developing large vocabulary continuous speech recognition system

A Study of Formation and Recognition of Speech in Speech Signal Processing
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I. SPEECH RECOGNITION AND OTHER PATTERN MATCHING PROBLEMS Another Substantial class of digital speech processing applications is worried about the Programmed extraction of data from the speech signal. Most

DEEP VARIATIONAL FILTER LEARNING MODELS FOR SPEECH RECOGNITION
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We present a novel approach to derive robust speech representations for automatic speech Recognition (ASR) systems. The proposed method uses an unsupervised data-driven modulation filter learning approach that preserves the key modulations of speech signal in

The Art of Developing Accurate Speech Recognition for Military Training
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Within the military, accurate, intelligible and consistent communication is critical for success. hen training military specific jargon such as that used for air traffic control (ATC), Controllers rehearse the way they will communicate with pilots and are necessarily

Application of Speech Recognition Technology on the Evaluation of English Pronunciation Teaching
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In order to better the current English learning environment and teaching mode so as to Improve the efficiency of spoken English learning, the speech recognition technology is Applied to the scoring of English pronunciation teaching. It is pointed out that the feature

ON USING 2D SEQUENCE-TO-SEQUENCE MODELS FOR SPEECH RECOGNITION
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Attention-based sequence-to-sequence models have shown promising results in automatic speech recognition . Using these architectures, one-dimensional input and output sequences re related by an attention approach, thereby replacing more explicit alignment processes

Inter speech 2018 Low Resource Automatic Speech Recognition Challenge for Indian Languages}}
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India has more than 15001 languages, with 30 of them spoken by more than one million Native speakers. Most of them are low-resource and could greatly benefit from speech and Language technologies. Building speech recognition support for these low-resource



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