SPEECH RECOGNITION IEEE PAPERS AND PROJECTS-2020
Speech recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to a machine-readable format. Rudimentary speech recognition software has a limited vocabulary of words and phrases, and it may only identify these if they are spoken very clearly.
Improvement of speech perception in quiet is an important goal of hearing aid provision. In practice, results are highly variable. The aim of this study was to investigate the relationship between type and extent of hearing loss (audiogram type), maximum word This paper proposes a novel flexible pressure sensor based on carbon black (CB), carboxy- methyl cellulose (CMC), and gelatin. CMC and gelatin are both foodgrade materials that are degradable, non-toxic, and environmentally friendly. Freeze drying is performed to obtain aJoint Training End-to-End Speech Recognition Systems with Speaker Attributes}}
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The end-to-end (E2E) model allows for simplifying the conventional automatic speech recognition (ASR) systems. It integrates the acoustic model, lexicon, and language model into one neural network. In this paper, we focus on improving the performance of the state-of Speech recognition is improved when the acoustic input is accompanied by visual cues provided by a talking face (Erber in Journal of Speech and Hearing Research, 12 (2), 423 42 1969; Sumby Pollack in The Journal of the Acoustical Society of America, 26 (2), 212In todays modern world, smart homes are now in demand. The times have now become the thing of the past since, over the last few decades, home automation has gained several milestones and acclaim, mainly limited to sci-fi videos. Compared with days gone by, home Natural language and human machine interaction is a very much traversed as well as challenging research domain. However, the main objective is of getting the system that can communicate in well-organized manner with the human, regardless of operational
Interactive Visualization of AI-based Speech Recognition Texts
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Speech recognition technology has achieved impressive success recently with AI techniques of deep learning networks. Speechto-text tools are becoming prevalent in many social applications such as field surveys. However, the speech transcription results are far
Improving speech recognition using bionic wavelet features
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Bionic wavelet transform is a continuous wavelet, based on adaptive time frequency technique. This paper presents a speech recognition system for recognizing isolated words by discretizing the continuous Bionic Wavelet (BW). Conversion from continuous to discrete
SURVEY ON AUTOMATIC SPEECH RECOGNITION
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ASR is an abbreviation of the term Automatic Speech Recognition . It is based on the voice as the research object. Speech recognition allows the machine to turn the speech signal into text or commands through the process of identification and understanding. The first speech
The Unintended Irregularities of Automatic Speech Recognition
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The present study examines the emerging role of Automatic Speech Recognition (ASR) and the unintended irregularities that arise when the algorithm is configured in healthcare practices. Once you consider health information technology, the mind often drifts towardsTraining procedures of a deep neural network are still an area with ample research possibilities and constant improvement either to increase its efficiency or its time performance. One of the lesser-addressed components is its objective function, which is an Athletes respiratory frequency and the physical energy consumption model based on speech recognition technology is presented in this paper. We use the series of rotation angles reflects changes in the electrical axis of the heart caused by breathing, and then
Towards Building a Cross-Lingual Speech Recognition System for Slovenian and Austrian German
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Methods of cross-lingual speech recognition have a high potential to overcome limitations on resources of spoken language in under-resourced languages. Not only can they be applied to build automatic speech recognition (ASR) systems for such languages, they canIn this paper, we describe the speech recognition from emotional speech . The task treated in this paper is not an emotion recognition from speech but a speech recognition ( speech to text) from a speech that contains distinct emotion. First, we compare two acoustic models
AUTOMATED SPEECH RECOGNITION SYSTEM WITH NOISE CONSIDERATION
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In the real world environment, Speech recognition is one of the most progressing research area. The quality as well as performance of automated speech recognition is disturbed by noises exist in the speech signals. Noises are inevitable in the speech that is transferred via wavelet packet decomposition is flexible in creating speeches identifiers by providing a variety of selections, each features selection will lead to generate a unique and small in size identifier, which can be used later on in any application requiring human speech recognition
PERFORMANCE ANALYSIS OF DIFFERENT ACOUSTIC FEATURES BASED ON LSTM FOR BANGLA SPEECH RECOGNITION
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In this work a new Bangla speech corpus along with proper transcriptions has been developed; also various acoustic feature extraction methods have been investigated using Long Short-Term Memory (LSTM) neural network to find their effective integration into a state
Investigation of Data Augmentation Techniques for Disordered Speech Recognition
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Disordered speech recognition is a highly challenging task. The underlying neuro-motor conditions of people with speech disorders, often compounded with co-occurring physical disabilities, lead to the difficulty in collecting large quantities of speech required for system
Speech Recognition and Optimization Using Linear Classification Artificial Neural Network
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This research studies the speech recognition process, and divides the speech recognition of linear system into four steps speech acquisition, training, classification and results. For each part, its optimization is given. First, the effects of different feature sets of the same speech on
An ensemble technique for speech recognition in noisy environments
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Automatic speech recognition (ASR) is a technology that allows a computer and mobile device to recognize and translate spoken language into text. ASR systems often produce poor accuracy for the noisy speech signal. Therefore, this research proposed an ensemble Purpose To investigate the impact of the amount of depressive symptoms in cochlear implant (CI) recipients on the development of speech recognition after CI-activation up to 2 years. Design Retrospective data analysis of a German short form of the Beck DepressionMachine Learning techniques gives computers the capability to learn using sample inputs and their outputs which creates a model to test against test cases instead of being explicitly programmed. Visual speech recognition is a process of conversion of speech to text in the
FEATURE LEARNING IN DEEP NEURAL NETWORKS STUDIES ON SPEECH RECOGNITION TASKS
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Automatic speech recognition (ASR) has been an active research area for more than five decades. Recent studies have shown that deep neural networks (DNNs) perform significantly better than shallow networks and Gaussian mixture models (GMMs) on large
Intelligibility and Automatic Speech Recognition (ASR) in Indonesian Accented English (IAE)
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Deterding and Mohamad (201 64) sum up Jenkins Lingua Franca Core features as follows All consonants, except /θ/, /ð/ and [?] Loss of intelligibility can be gauged with Automatic Speech Recognition (ASR), Ahn Lee (2016) portray how Google Voice as ASR
Speech Recognition Using Machine Learning: A
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Speech it sa pressure wave that travel through the air created by the vibration of the larynx by the opening and the closing of mouth and human experiments on speech recognition programs using machine learning techniques. In this paper various speech types andThis paper studies three feature extraction methods, Mel-Frequency Cepstral Coefficients (MFCC), Power-Normalized Cepstral Coefficients (PNCC), and Modified Group Delay Function (ModGDF) for the development of an Automated Speech Recognition SystemSpeech Recognition is the ability of the machine to identify the word or phrases in human language and convert it into machine understandable form. Speech Recognition allows you to supply input to an application together with your voice. Speech recognition systems aim to
End-to-End Machine Learning for Speech Processing: Speechto- Speech Speechto-Text and Text-to- Speech
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AUTOMATIC SPEECH RECOGNITION TECHNIQUES
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Automatic speech recognition is ubiquitous in the modern era of technology. So many techniques have been proposed in the past for automatic recognition of speech in various languages. In this study we have summarized various propose speech recognition
AUTOMATIC SPEECH RECOGNITION SYSTEM USING MFCC-BASED LPC APPROACH WITH BACK PROPAGATED ARTIFICIAL NEURAL NETWORKS
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Over the previous years, a marvelous quantity of study was performed by utilizing the artificial intelligence based deep learning approaches for the speech recognition applications. The automatic speech recognition (ASR) facing the problems in as
Discriminative Multi-modality Speech Recognition Supplementary Material
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The following supplementary material includes the details: 1) P3D network (Sec. 1); 2) EleAtt- GRU block (Sec. 2); 3) examples of AE and AE-MSR speech recognition results (Sec. 3); 4) enhancement examples of the AE networks (Sec. 4); 5) examples of mouth crop (Sec. 5); 6)
Deep Learning Based Dereverberation of Temporal Envelopes for Robust Speech Recognition
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Automatic speech recognition in reverberant conditions is a challenging task as the long- term envelopes of the reverberant speech are temporally smeared. In this paper, we propose a neural model for enhancement of sub-band temporal envelopes for
Speech Recognition for ATCO assistance
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In January together with the latest update of an office software package, speech recognition became part of the standard computer equipment for DFS employees. Since this installation, every employee could use it to dictate eg emails just by simple button pressThis paper describe an implementation of application which is developed to find malignant partially malignant thyroid detection using patients speech signal variations. It detects Thyroid by persons speech samples. Format of Speech sample files are. wav (wave audioAdvancements in Biomedical field provide lot of assistive devices to help deaf people and visually impaired people. When it comes to the subject of deaf-blind people (who losses both hearing and visual ability) availability of such devices are very limited. Tactile signaling
Towards Robust Combined Deep Architecture for Speech Recognition : Experiments on TIMIT
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Over the last years, many researchers have engaged in improving accuracies on Automatic Speech Recognition (ASR) task by using deep learning. In state-of-theart speech recognizers, both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) based
Word-level Speech Recognition with a Letter to Word Encoder
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We propose a direct-to-word sequence model which uses a word network to learn word embeddings from letters. The word network can be integrated seamlessly with arbitrary sequence models including Connectionist Temporal Classification and encoder-decoder
Fuzzy Speech Recognition : A Review
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The area of speech recognition is one of the interesting field in speech signal processing. Achieving accuracy and robustness is a very difficult constraint to various environmental factors. Progressive work and reviews in the speech recognition application has been
Neural Incremental Speech Recognition Through Attention Transfer
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One of the challenges that have to be confronted to achieve a simultaneous speech translation system is the incremental ASR (ISR) development. Hidden Markov model (HMM) ASR [ 5] performs a low-delay recognition but it cannot do an end-toend modeling. TheSpeech analysis is still a challenging area in recognition classification and retrieval and it is one of the most interesting fields in Digital Signal Processing. Many researches already done on it that is based on different scientific programs and materials tools and that produce The main objective of this research paper is the use of recurrent neural networks (RNNs) to arise a productive solution to problems in authentication with electrocardiogram (ECG)- based biometrics and Speech Recognition . This paper investigates deep recurrent neural
Sociolinguistic Variation and Automatic Speech Recognition : Challenges and Approaches
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Page 1. Sociolinguistic Variation and Automatic Speech Recognition : Challenges and Approaches Dr. Rachael Tatman Page 2. @rctatman Who am I Dr. Rachael Tatman PhD in Linguistics (2017): Modeling the Perceptual Learning of Novel Dialect Features ? Commercial automatic
TImproving Students Pronunciation Using Auto Speech Recognition Software to the Tenth Grade Language Major Students at SMAN 1 Lawang
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Clear pronunciation is important in order to communicate well in English. Unfortunately, the ten grader students of language major at SMAN 1 Lawang find difficulties to pronounce English words in classroom activity. This research is classroom action research because the
A Comparative Study of Mel-LPC Based Bangla Speech Recognition Under Clean and Noisy Condition
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This paper shows the comparison between noisy and clean data for recognition of Bangla speech . The used database consists of two sets of data one is for training containing 3824 utterances of Bangla digit sequences of 20 male and 20 female speakers and the other one
FusionRNN: Shared Neural Parameters for Multi-Channel Distant Speech Recognition
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Distant speech recognition remains a challenging application for modern deep learning based Automatic Speech Recognition (ASR) systems, due to complex recording conditions involving noise and reverberation. Multiple microphones are commonly combined with wellSpeech Recognition is the process by which a computer can recognize spoken words. Basically it means talking to your computer and having it correctly recognize what you are saying. This work is to improve the speech recognition accuracy for Telugu language usingSpeech Recognition system is the application of Natural Language Processing (NLP), a major area Artificial Intelligence (AI) research, that identify words and phrases in spoken language and convert them to a machine readable format and explores how computers can This paper demonstrates the effect of incorporating Deep Neural Network techniques in speech recognition systems. Speech recognition through hybrid Deep Neural Networks on the Kaldi toolkit for the Punjabi language is implemented. Performance of the automatic
Speech to Text System: Pastor Wang Mandarin Bible Teachings ( Speech Recognition )
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Motivation of this system comes from an aspiration to help a large study group ease everyday life. The group aims to read through The Holy Bible at pace of one chapter a day along with a pastors teachings. Pastor Wang has been recording audio, chapter-by-chapter teachings basedThis article liberalize the current machine learning rehearses as utilized in the emerging edge and as noteworthy to speech recognition approaches on present-day surgical robots. The desire is to advance the development of medical robots among the machine learning
Speech Interface for Form Filling with Biometric Recognition and Authentication
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Speech recognition technology has several real-world applications and security is imp for this paper discusses the algorithm that is used in speech to text online form fill up application for different organization. Our aim to aware more people to know about this technology has
HINDI SPEECH AUDIO VISUAL FEATURE RECOGNITION
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Abstract Automatic Speech Recognition (ASR) system is designed to perform well with some constrained and unfavorable conditions. However, under noisy conditions its performance deteriorates with increase in noise level. Features of Audio-visual of ASR have an important
ANALYSIS OF SPEECH RECOGNITION USING CONVOLUTIONAL NEURAL NETWORK
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Nowadays in the current world, speech recognition has gained prominence and use with the rise of AI and intelligent assistants, such as Amazon Alexa, Apple Siri, Microsoft Cortana, Google assistant. Speech recognition is the ability of a machine or a program to identify
MINUTE MEETING SYSTEM USING SPEECH RECOGNITION
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This project is about minute meeting system using speech recognition to help people who is in charge in writing to write smoothly and efficiently. Usually, when it comes to write minute meeting, you have to write or type faster to catch up all the important information but not
Speech Representation Learning for Emotion Recognition Using End-to-End ASR with Factorized Adaptation
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Developing robust speech emotion recognition (SER) systems is challenging due to small- scale of existing emotional speech datasets. However, previous works have mostly relied on handcrafted acoustic features to build SER models that are difficult to handle a wide range of
Application of Hidden Markov Models in Speech Command Recognition
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In this study, vector quantization and hidden Markov models were used to achieve speech command recognition . Pre-emphasis, a hamming window, and Mel-frequency cepstral coefficients were first adopted to obtain feature values. Subsequently, vector quantization
METHODS OF LANGUAGE AND ACOUSTIC MODELLING IN SPEECH RECOGNITION
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The research deals with the problem of efficiency of traditional and modern methods of automatic speech recognition (ASR). In the article the analysis of the common machine speech recognition algorithm structure is conducted, particularly language and acoustic
Speech recognition using very deep neural networks: Spectrograms vs Cochleagrams
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We compare the performance of deep neural networks performing a speech recognition task on a benchmark dataset provided by (TensorFlow). We are training two deep convolutional neural networks on different spectral representations of the speech data
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