speech recognition IEEE PAPER 2022


Speech recognition, or speech-to-text, is the ability of a machine or program to identify words spoken aloud and convert them into readable text. Rudimentary speech recognition software has a limited vocabulary and may only identify words and phrases when spoken clearly. Speech recognition technologies such as Alexa, Cortana, Google Assistant and Siri are changing the way people interact with their devices, homes, cars, and jobs. The technology allows us to talk to a computer or device that interprets what we're saying in order to respond to our question or command







An Evaluation on Speech Recognition Technology based on Machine Learning
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Speech is the basic way of interaction between the listener to the speaker by voice speech recognition to interact with machines to humans. The language used for speech recognition

Speech Recognition During Follow-Up of Patients with M nières Disease: What Are We Missing
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speech recognition is also affected, it has not been extensively studied. The objective of the study was to describe speech recognition of performing speech recognition tests during

Effects of Speaking Rate on Speech and Silent Speech Recognition
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speech recognition performance. Besides, no such investigations have been conducted for silent speech recognition . interact differently with speech and silent speechbased methods,

Cross-Modal Mutual Learning for Audio-Visual Speech Recognition and Manipulation
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: To evaluate the proposed framework for visual and audio speech recognition we adopt top-1 accuracy as the metric and report the results in Table 4. In this table, we compare our

End-to-End Speech Recognition of Tamil Language
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Abstract: Research in speech recognition is progressing with Speech Recognition (ASR) for languages with low resources. We present a method to develop speech recognition model

Development of Speaker-Independent Automatic Speech Recognition System for Kannada Language
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The continuous Kannada speech recognition system has been demonstrated in this study. The speech data have been collected, transcribed and checked with the transcription system.

Lexical competition and predictive certainty in speech recognition
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A growing body of evidence suggests that speech recognition only the first 2-3 speech sounds of words were available to models of speech perception and spoken word recognition .

Efficient Multi-angle Audio-visual Speech Recognition using Parallel WaveGAN based Scene Classifier
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and improves speech recognition accuracy significantly. AVSR must conduct image processing in addition to speech speech only ASR is performed to obtain speech recognition

Speech Recognition beyond isolated English sentences
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and Speech Processing draj@cs.jhu.edu Streaming multi-talker speech recognition Instead, we leverage shared phonemic outputs by using annotated speech in other languages (think

Speech RecognitionBased Automated Visual Acuity Testing with Adaptive Mel Filter Bank
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Centered on a cognitively motivated attribute extraction and speech recognition approach, this paper proposes a novel idea that immediately determines the eyesight deficiency. The

Global Cognitive Function is Associated with Sex, Educational Level, Occupation Type, and the Speech Recognition Rate in Older Chinese Adults: A Single
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A combination of otoscopy, acoustic immittance, pure-tone audiometry, and speech audiometry was The speech recognition rate was determined with speech audiometry. The acoustic

Multilingual Speech Recognition Based on The End-to-End Framework
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Speech recognition is an important field in natural language processing. In this paper, the end-to-end framework for speech recognition In order to compare speech recognition methods

BEING GREEDY DOES NOT HURT: SAMPLING STRATEGIES FOR END-TO-END SPEECH RECOGNITION
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Maximum Likelihood Estimation (MLE) is currently the most common approach to train large scale speech recognition systems. While it has significant practical advantages, MLE

INTELLIGENT COMMUNICATION SYSTEM BASED ON AUTOMATIC SPEECH RECOGNITIONCOMPILING CORPUS OF PHRASEOLOGY
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the application of automatic speech recognition in the field of based on automatic speech recognition that will allow the system based on automatic speech recognition . Then, we

Large Comparative Study of Recent Computational Approach in Automatic Hate Speech Detection
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use several recent hate speech datasets from different hate speech recognition studies. The in hate speech detection problems thus presenting an interesting benchmark hate speech Abstract Automatic emotion recognition from speech plays knowledge from speech for effective emotion recognition multiple speech representations for emotion recognition . In

Long-Term Effects of Hearing Aid Use on Auditory Spectral Discrimination and Temporal Envelope Sensitivity and Speech Perception in Noise.
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Spectral peak resolution and speech recognition in quiet: normal hearing, hearing impaired, and cochlear implant listeners. J Acoust Soc Am. 2005;118(2):1111-1121.This sort of recognition is supposed to be used to extract useful of speech recognition systems. The model of SER includes the discrete speech emotion model and continuous speech

OBJECT RECOGNITION BY VISUALLY IMPAIRED USING MACHINE LEARNING: A STUDY
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Machine learning can be has got huge number of applications such as Image Recognition Speech Recognition Self-driving cars, Virtual Personal Assistant, etc. Machine Learning has

MONICA2: Mobile Neural Voice Command Assistants towards Smaller and Smarter
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speech recognition . MONICA2 reduces the number of parameters of deep neural network by 58%, with minimal recognition yet accurate on-device speech recognition which could be

Do self-supervised speech models develop human-like perception biases
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Recent advances in speech recognition and representation learning show that self-supervised pre-training is an excellent way of improving performance while reducing the amount of

Flow-based Unconstrained Lip to Speech Generation
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coarse but more intelligible speech based on aligned visual more realistic speech conditioned on the coarse speech . Such for speech perception and automatic speech recognition .

A MULTITASK LEARNING FRAMEWORK FOR SPEAKER CHANGE DETECTION WITH CONTENT INFORMATION FROM UNSUPERVISED SPEECH
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[15] Herbert Gish, Man-Hung Siu, and Robin Rohlicek, Segregation of speakers for speech recognition and speaker identification., in ICASSP, 199 vol. 9 pp. 873 876.

ANALYSIS ON VOWEL/E/IN MALAY LANGUAGE RECOGNITION VIA CONVOLUTION NEURAL NETWORK (CNN)
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This technique, treating the severity of speech disorder patients, was among the earliest speech The dialect element of voice and speech recognition has been the subject of numerous

The Information Channels of Emotion Recognition : A Review
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(ie, Speech facial expressions, ) or implicitly (ie, body language, text, ). Emotion recognition has A review on emotion recognition is given in this article. The survey seeks single and

A Reinforcement Learning Approach to Speech Category Acquisition
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speech sounds and demonstrates that our algorithm mimics second language learners improvement on discrimination of a non-native speech role in effective speech category learning

Library Management Using Voice Assistant
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The design aspects of a speech recognition engine configured for mobile devices are which support speech recognition . We will be using the speech recognition library because Automated music genre recognition has been in works In this paper, an interesting approach for music emotion recognition has and is being used for MIR as well as speech recognition .

Speech Perception and Enhancement in Cochlear Implants (Part II)
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speech to the enhanced one with improved quality, intelligibility, automatic speech recognition Assume clean speech noise spectra are Rayleigh and Gaussian distributions, we have

GuidedMix: An on-the-fly data augmentation approach for robust speaker recognition system
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was recently proposed for speech recognition, and speaker recognition such as speaker recognition . Inspired by Cutmix, which was applied in computer vision and speech recognition [

Dialogue Act Recognition using Visual Information
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Dialogue Act (DA) recognition is a task to segment a dialogue into is a speech signal which is usually converted into textual representation using an Automatic Speech Recognition (ASR

A HYBRID CNN-BILSTM MODEL FOR DRUG NAMED ENTITY RECOGNITION
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Bucketing is most suitable for training tasks such as speech recognition language modelling, and other machine learning tasks using GRU and LSTM. In all scenarios, we also applied

Classification of patients using heart sound and MFCC
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Various research fields of sound processing include speech recognition speaker recognition speech synthesis, and speech encoding. In this paper, healthy people and patients were

Design and implementation of smart voice assistant and recognizing academic words
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In addition, a speech recognition system is created in order to recognize the significant technical words. An artificial neural network (ANN) with different structure networks and training

Computational Modeling of Intonation Patterns in Arabic Emotional Speech
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speech utterances and emotional ones. This approach could facilitate the creation of tonal inventories of speech and for the development of speech emotion recognition (SER) features

A Systematic Review of Current Research on Affordances and Challenges of Technology-Assisted Gram Learning
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potentials of automatic speech recognition technology and the affordance of automatic speech recognitionbased CALL the efficacy of the automatic speech recognition technology.

LISTEN, KNOW AND SPELL: KNOWLEDGE-INFUSED SUBWORD MODELING FOR IMPROVING ASR PERFORMANCE OF OOV NAMED ENTITIES
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Automatic speech recognition (ASR) is increasingly being used in specialized domains such as medical ASR and news transcription. Owing to the lack of high quality annotated speech

Unsupervised Machine Learning Method: A case for Hidden Markov Models with an Application to Criminal Intelligence
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HMMs which were initially developed and applied within the context of speech recognition have received widespread attention and application in other disparate fields. The theoretical

Human Activity Recognition : A Spatio-temporal Image Encoding of 3D Skeleton Data for Online Action Detection
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Cacnet: Cube attentional cnn for automatic speech recognition . InInternational Joint Conference on Neural Networks (IJCNN), pages 1 7. Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y. This book is mainly intended for researchers working in the area of multilingual speech recognition . This book will be useful for the young researchers who want to pursue research in

Speech Prosody: The Musical, Magical Quality of Speech
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These days, I work on voice synthesis, speech recognition and acoustic perception, recognition and sensation! I am interested not only in language and music, but in the intersection automatic speech recognition (ASR) is quite helpful remedy in in developing several speech recognition systems. Because a stationary signal, HMMs are utilised in speech recognition .

The Effectiveness of Mobile-assisted Language Learning (MALL) Applications on the Spoken English Assessments in Chinas Universities
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assessment application characterised with automatic speech recognition (ASR) system on the improvement of complexity, accuracy, and fluency of English learners in China s colleges.voice recognition modules are used for controlling automotive vehicles. Speech recognition The basic components of the speech recognition system are shown in Figure 18.13. The

REAL-TIME APPLICATIONS OF SVM
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SPEECH RECOGNITION Speech recognition is utilized for isolating individual words out of and models were trained effectively Using speech recognition we could make a connection

The Effectiveness of Mobile-Assisted Language Learning (MALL) Applications on the Spoken English Assessments in Chinas Universities
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application characterised with Automatic Speech Recognition (ASR) system on the improvement of complexity, accuracy, and fluency of English learners in China s colleges.

A Reversible Convolutional Neural Network Model for Sign Language Recognition
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Therefore, sign language recognition (SLR) has attracted much interest to bridge the large making accurate recognition . Many various types of sign language recognition (SLR)

Artificial Intelligence: The Future of Employment
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Artificial Intelligence is the theory and growth of computer systems, which can do jobs that generally, needed human intelligence, such as decision-making, visual perception, speech

Mapping and Analysis of Standard Indonesian Pronunciation Errors by Using the Bigram Method
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The main stages of speech recognition consist of several main stages, namely preprocessing, feature extraction, acoustic models, language models, pattern classification and severalpopular owing to improvements in automatic speech recognition . However, the understanding of of possible executable commands and about the overall accuracy of voice recognition .

Chasing the conversation: Autistic experiences of speech perception
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Speechperception tasks used in children have tended to rely upon single-word or single- in which the speech is embedded have ranged from simple white noise to speech babble .

Interpretable high-level features for human activity recognition
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Continuous speech recognition via centisecond acoustic states. The Journal of the Acoustical Society of America, 59(S1):S97 S97. Chen, C., Liaw, A., and Breiman, L. digital revolution of automatic speech recognition . In this paper, the user interface which are more suitable for people are discussed. The automatic speech recognition is subset of NLP.

Pronunciation Adaptive Self Speaking Agent Using WaveGrad
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Due to the limit of the number of API calls, we could not use Google s speech recognition in this work.

Multi-channel EEG-based emotion recognition in the presence of noisy labels
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Emotion recognition occupies an important position in the field of artificial intelligence . Correct emotion representation is a key step in emotion recognition research, which can be

Fine-tuning Wav2vec2. 0 on caption data
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We discovered in section 4.4 that speech recognition became easier for Wav2Vec2 when we shifted the start- and endtimes of the caption labels to become more synchronized with the

Analysis of the Combination of AR Technology and Translation System
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recognition (ASR), machine translation, and speech synthesis The first is to use Automatic speech recognition components to process the original speech . There are many existing

Distinguishing Homophenes using Multi-head Visual-audio Memory for Lip Reading
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Speech Recognition (VSR), is a task that recognizes speech technology for audiobased speech recognition in a noisy to audio-based Automatic Speech Recognition (ASR), it is

First Attempt of Gender-free Speech Style Transfer for Genderless Robot
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gender-free speech audio from text with genderless speech style embedding as the condition. We trained a speech gender recognition model for speech gender style embedding. The

A practical wearable sensor-based human activity recognition research pipeline
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time recognition performance. A shorter step-size of windows shift results in a shorter delay of the recognition outcomes, but the interim recognition applications in speech recognition . In

Speech Recognition IEEE PAPER 2021




Using Radio Archives for Low-Resource Speech Recognition : Towards an Intelligent Virtual Assistant for Illiterate Users
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For many of the 700 million illiterate people around the world, speech recognition technology could provide a bridge to valuable information and services. Yet, those most in need of this technology are often the most underserved by it. In many countries, illiterate 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 automaticPurpose 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 Depression

Malayalam Speech Recognition
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The project is based on the development of state-of-the-art large vocabulary continuous speech recognition (LVCSR) system for the Malayalam language. Problems of existing speech recognition are lack of accuracy and misinterpretation, time cost and productivity

A Brief Analysis of the Dual Influences of Speech Recognition Assistance on Simultaneous Interpretation
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In the era of artificial intelligence, big data, speech recognition machine translation and other technologies have brought an increasing impact on the field of manual translation. On the one hand, it challenges manual translation, and on the other hand, it assists andThe process of speech recognition is such that a speech signal from a client or user is received by the system through a microphone, then the system analyses this signal and extracts useful information from the signal which is converted to text. This study focuses on

Classification approaches for automatic speech recognition system
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Recognition of Speech is now becoming more widespread. Different applications that are knowledgeable of interactive expression are present on the market. For those devices in which handwriting is complicated, speaking recognition systems are sensible options. With

Multilingual Sequence-To-Sequence Speech Recognition
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There are a multitude of languages and dialects in the world. To develop a speech recognition system for all of them is an expensive and time-consuming task. Multilingual systems can be advantageous here, but dont work as good as monolingual ones currently

Speech Recognition
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Speech is a simple and effort less approach of communication amongst humans, but in this day and age humans are not restricted to connecting to one another but even to the various machines in their lives. The most essential being the computer. So, this communication

Assessing the Practicality of Using an Automatic Speech Recognition Tool to Teach English Pronunciation Online
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This study aims to determine how well an automatic speech recognition (ASR) tool could be used in an online EFL course to help L1 Japanese students improve their pronunciation. Previous studies have suggested that ASR tools can be helpful in this regard, but few have

Myan Continuous Speech Recognition System Using Convolutional Neural Network
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Translating the human speech signal into the text words is also known as Automatic Speech Recognition System (ASR) that is still many challenges in the processes of continuous speech recognition . Recognition System for Continuous speech develops with the fourThe paper presents experiments on the use of automatic speech recognition for diagnostic evaluation of synthetic speech. Our previous work on the topic showed a strong correlation between the subjective and objective evaluation (ITU-T Rec. P. 862 PESQ) of the quality of

The convolutional neural networks for Amazigh speech recognition system
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In this paper, we present an approach based on convolutional neural networks to build an automatic speech recognition system for the Amazigh language. This system is built with TensorFlow and uses mel frequency cepstral coefficient (MFCC) to extract features. In order

A STUDY ON SPEECH RECOGNITION
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The primary mode of communication is language, and speech is its primary medium. Speech is the most fundamental form of human communication. Speech recognition is a way of converting speech sounds into text. Nonetheless, there are many important research

Speech Recognition Technology for OPAC Service: An Innovative Idea for Indian Libraries
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The Information and Communication Technology (ICT) driven environment compelled to software engineers and professionals to work harder to meet out the expectations of the people needs and requirements. The smartphone savvy generation is more acquainted with

Concatenative Speech Recognition using Morphemes
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This paper adopts a novel sub-lexical approach to construct viable continuous speech recognition systems with scalable vocabulary that use the components of words to form the elements of pronunciation dictionaries and recognition lattices. The proposed Concatenative

HMM-based phoneme speech recognition system for the control and command of industrial robots. Technical
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In recent years, the integration of human-robot interaction with speech recognition has gained a lot of pace in the manufacturing industries. Conventional methods to control the robots include semi-autonomous, fully-autonomous, and wired methods. Operating through

Evaluating the Vulnerability of End-to-End Automatic Speech Recognition Models To Membership Inference Attacks
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Recent studies have shown that it be possible to determine if a machine learning model was trained on a given data sample, using Membership Inference Attacks (MIA). In this paper we evaluate the vulnerability of state-of-the-art speech recognition models to MIA

Speech Recognition using Deep Canonical Correlation Analysis in Noisy Environments.
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In this paper, we propose a method to improve the accuracy of speech recognition in noisy environments by utilizing Deep Canonical Correlation Analysis (DCCA). DCCA generates projections from two modalities into one common space, so that the correlation of projected

Speech recognition (Respeaking) vs. the conventional method (Keyboard): A quality-oriented comparison of speech-to-text interpreting techniques and
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Speech-to-text interpreting (STTI) is especially in the German-speaking area still a young profession, but it is used more and more, not least thanks to the UN Convention on the Rights of Persons with Disabilities and the national legislations that derive from it. Over time

Automatic Speech Recognition systems errors for accident-prone sleepiness detection through voice
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Excessive Daytime Sleepiness (EDS), a symptom linked to chronic sleepiness, impacts everyday life and increases risks of work or road accidents of subjects affected by it. The detection of accident-prone EDS through voice benefits from its ease to be implemented in

A SURVEY PAPER ON SPEECH RECOGNITION SYSTEM
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By composing in the inquiry choice bar, it helps in looking through the applications in windows 7. There is no such application inherent windows that can serve to convert discourse into text. Discourse acknowledgment is a mind boggling order undertaking and

Is Speech Recognition Software a Viable Future for Dysarthric Speakers A Critical Review
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The idea of speech recognition software, in its purest form opens the possibilities for individuals with dysarthria to bypass one of their biggest barriers, being difficulties with speech clarity. This critical review explored the relationship between speech to text software

Filter Bank Effects on Detecting Adversarial Speech Recognition Attacks In Noise
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In an existence of ever more internet connected devices and increasing number of input modalities, systems employing visionary and auditory modes of interaction with the world, become ever so slightly more exposed to adversarial behaviour. A very recent vision

Integration of Audio video Speech Recognition using LSTM and Feed Forward Convolutional Neural Network
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In the current scenario, audio visual speech recognition is one of the emerging fields of research, but there is still deficiency of appropriate visual features for recognition of visual speech. Human lip-readers are increasingly being presented as useful in the gathering ofIn this work we perform automatic recognition of continuous speech signal spoken in Gujarati language using machine learning (ML) technique. For this purpose, from continuous speech signal of sentence we first extract words using short term auto-correlation

Speak2Code: A Multi-Utility Program based on Speech Recognition that Allows you to Code Through Speech Commands
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Voice recognition has gained prominence and extensive use with the rise of Artificial Intelligence and that of the intelligent assistants such as Amazons Alexa, Apples Siri and Microsofts Cortana. Voice recognition systems enable Coders to interact with IDEs, Coding

Deep Scattering and End-to-End Speech Models towards Low Resource Speech Recognition
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Automatic Speech Recognition (ASR) has made major leaps in its advancement largely due to two different machine learning models: Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). State-of-the art results have been achieved by combining these two

EMOTION BIAS IN AUTOMATIC SPEECH RECOGNITION
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In this paper, we investigate the effect of emotions on an established automatic speech recognition system using five emotional speech databases covering English, German, and Italian language. We computed the word error rates and the significance of the ratio between

Augmenting Harper Valley Bank: Robust Automatic Speech Recognition
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Abstract The newly introduced Harper Valley Bank Dataset serves as a testbed for techniques in the field of spoken language processing. In this paper, we tackle LASs tendency to overfit on the HVB dataset by using a combination of augmentation techniques

Improved text normalization and language models for SpeeDs Automatic Speech Recognition System
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Automatic speech recognition (ASR) systems that use word-based language models require periodical updates to include new named entities (eg. coronavirus, COVID-19) or collocations. Moreover, in particular for the Romanian language, the new hyphenated words

REVIEW ON SMALL VOCABULARY AUTOMATIC SPEECH RECOGNITION SYSTEM (ASR) FOR MARATHI
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Speech interfaces make available people an easy and comfortable means to interact with human and Laptop Systems or PC. This paper is an attempt to study the Small Vocabulary Speech Recognition System for Marathi Using Automatic Speech Recognition System

Automatic Speech Recognition in the French
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For millennia, speech has been the most natural communication tool for humans. As a result, with the emergence of computer science and signal processing, tremendous efforts have been made in the field of Automatic Speech Recognition (ASR). In fact, it has been an active

TOWARDS RELIABILITY-GUIDED INFORMATION INTEGRATION IN AUDIO-VISUAL SPEECH RECOGNITION
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Audio-visual speech recognition can improve the recognition rate in many small-vocabulary tasks. But for large vocabularies, due to difficulties like unsatisfactory lipreading accuracies, improving the recognition rate over audioonly baselines remains difficult. In this work, we

Using a logical Derivative to Determine the Information Content of Object Properties in Speech Recognition Tasks
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This paper offers an approach for determining the information content of object properties in recognition tasks. The scope of this approach is not the subject area where objects and characteristics of these objects are specified, but a trained ΣΠ-neural network that works

TRIBUS: An end-to-end automatic speech recognition system for European Portuguese
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End-to-end automatic speech recognition (ASR) approaches have emerged as a competitive alternative to traditional HMM-based ASR systems. Unfortunately, most end-to- end ASR systems are not easily reproduced since they require vast amounts of data and

Investigating the scarce data and resources problem for speech recognition using transfer learning and data augmentation
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We investigate the effect of the transfer learning procedure on e2e Automatic Speech Recognition systems using a limited amount of data. We use a DeepSpeech inspired base- line in our experiments and based on different transfer learning techniques. Our

Indigenuous Vocabulary Reformulation for Continuousyorùbá Speech Recognition In M-Commerce Using Acoustic Nudging-Based Gaussian Mixture Model
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One of the current research areas is speech recognition by aiding in the recognition of speech signals through computer applications. In this research paper, Acoustic Nudging,(AN) Model is used in re-formulating the persistence automatic speech recognition

GMM and LDA based Speech recognition using Sonogram
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Automatic recognition of speech using computers is a challenging issue. This paper describes a techniques that uses Gaussian mixture model (GMM) and Linear Discriminate Analysis (LDA) to recognized speech based on features using Sonogram. Modeling

AUTOMATIC-SUBTITLING: COMPARISON ON THE PERFORMANCE OF FORCED ALIGNMENT AND AUTOMATIC SPEECH RECOGNITION
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This work is focusing on the automatic generation of subtitles using different tools that can be categorized as Forced Aligners (FAs) or Automatic Speech Recognizers (ASRs). A comparison of the performance of FA and ASR for the task of generating same-language




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