Indian Sign Language Recognition Using Deep Learning Techniques

By automatically translating Indian sign language into English speech, a portable multimedia Indian sign language translation program can help the deaf and/or speaker connect with hearing people. It could act as a translator for those that do not understand sign language, eliminating the need for a mediator and allowing communication to take place in the speaker's native language. As a result, Deaf-Dumb people are denied regular educational opportunities. Uneducated Deaf-Dumb people have a difficult time communicating with members of their culture. We provide an incorporated Android application to help ignorant Deaf-Dumb people fit into society and connect with others. The newly launched program includes a straight forward keyboard translator that really can convert any term from Indian sign language to English. The proposed system is an interactive application program for mobile phones created with application software. The mobile phone is used to photograph Indian sign language gestures, while the operating system performs vision processing tasks and the constructed audio device output signals speech, limiting the need for extra devices and costs. The perceived latency between both the hand signals as well as the translation is reduced by parallel processing. This allows for a very quick translation of finger and hand motions. This is capable of recognizing one-handed sign representations of the numbers 0 through 9. The findings show that the results are highly reproducible, consistent, and accurate

2-D Attention Based Convolutional Recurrent Neural Network for Speech Emotion Recognition

Recognizing speech emotions  is a formidable challenge due to the complexity of emotions. The function of Speech Emotion Recognition(SER) is significantly impacted by the effects of emotional signals retrieved from speech. The majority of emotional traits, on the other hand, are sensitive to emotionally neutral elements like the speaker, speaking manner, and gender. In this work, we postulate that computing deltas  for individual features maintain useful information which is mainly relevant to emotional traits while it minimizes the loss of emotionally irrelevant components, thus leading to fewer misclassifications. Additionally, Speech Emotion Recognition(SER) commonly experiences silent and emotionally unrelated frames. The proposed technique is quite good at picking up important feature representations for emotion relevant features. So here is a two  dimensional convolutional recurrent neural network that is attention-based to learn distinguishing characteristics and predict the emotions. The Mel-spectrogram is used for feature extraction. The suggested technique is conducted on IEMOCAP dataset and it has better performance, with 68% accuracy value

A Novel BCI - based Silent Speech Recognition using Hybrid Feature Extraction Techniques and Integrated Stacking Classifier

1165-1176The Brain Computing Interface (BCI) is a technology that has resulted in the advancement of Neuro-Prosthetics applications. BCI establishes a connection between the brain and a computer system, primarily focusing on assisting, enhancing, or restoring human cognitive and sensory - motor functions. BCI technology enables the acquisition of Electroencephalography (EEG) signals from the human brain. This research concentrates on analyzing the articulatory aspects, including Wernicke's and Broca's areas, for Silent Speech Recognition. Silent Speech Interfaces (SSI) offers an alternative to conventional speech interfaces that rely on acoustic signals. Silent Speech refers to the process of communicating speech in the absence of audible and intelligible acoustic signals. The primary objective of this study is to propose a classifier model for phoneme classification. The input signal undergoes preprocessing, and feature extraction is carried out using traditional methods such as Mel Frequency Cepstrum Coefficients (MFCC), Mel Frequency Spectral Coefficients (MFSC), and Linear Predictive Coding (LPC). The selection of the best features is based on classification accuracy for a subject and is implemented using the Integrated Stacking Classifier. The Integrated Stacking Classifier outperforms other traditional classifiers, achieving an average accuracy of 75% for both thinking and speaking states on the KaraOne dataset and approximately 86.2% and 84.09% for thinking and speaking states on the Fourteen Channel EEG for Imagined Speech (FEIS) dataset

Latent Semantic Indexing Based SVM Model for Email Spam Classification

437-442Internet plays a drastic role in part of communication nowadays but in e-mail, spam is the major problem. Email spam is unwanted, inappropriate or no longer wanted mails also known as junk email. To eliminate these spam mails, spam filtering methods are implemented using classification algorithms. Among various algorithms, Support Vector Machine (SVM) is used as an effective classifier for spam classification by various researchers. But, the accuracy level is not up to notable level so further. To improve the accuracy, Latent Semantic Indexing (LSI) is used as feature extraction method to select the suitable feature space. The hybrid model of spam mail classification can provide the effective results. The Ling spam email corpus is used as datasets for the experimentation. The performance of the system is evaluated using measures such as recall, precision and overall accuracy

An Ensembled Classifier for Email Spam Classification in Hadoop Environment

Email is one of the most ubiquitous and pervasive application used on a daily basis by millions of people worldwide. Email spam is a serious worldwide problem which causes problems for almost all computer users. Nowadays, e-mail becomes a powerful tool for communication as it saves a lot of time and cost. But, due to social networks and advertisers, most of the e-mails contain unwanted information called spam. Spam is the unwanted and unsolicited commercial e-mail. It is also known as junk e-mail. This issue not only affects normal users of the internet, but also causes a huge problem for companies and organizations since it costs a huge amount of money in lost productivity, wasting user’s time and network bandwidth. Recently, various researchers have presented several email spam classification techniques. Spam classifications, which filter the spam emails from inbox moves it to our junk email folder. It automatically classifies email based on the social features. Spam classifies the set of mails into spam and ham based on its contents. It is very difficult to eliminate the spam mail completely as the spammers change their techniques frequently. The proposed system, we have developed is an efficient technique to classify the email spam using ensemble method. Gradient Boost classification is used which is an ensemble of the weak decision tree and weighted majority voting is used to ensemble the decision tree and also Naive Bayes classification is used. It consists of two phases, such as training phase and testing phase. The performance metrics namely precision, recall and accuracy are used for evaluation

An automated & enhanced epileptic seizure detection based on deep learning based architecture

A precise seizure detection system allows epileptic patients to receive early warnings before a seizure occurs. It is critical for people who are drug-resistant. To find the very minimal time before seizure onset, traditional seizure prediction techniques rely on variables collected from electroencephalography (EEG) recordings and classification algorithms. Such methods cannot achieve high-accuracy prediction due to the information loss of hand-crafted features and the limited classification capabilities of regression and other algorithms. Kernels are employed in the early and late stages of the CNN RNN architecture with VGG 16 in the convolution and max-pooling layers, respectively. The suggested hybrid model is tested using the CHB-MIT scalp EEG datasets. The total sensitivity, false prediction rate, and area under the receiver operating characteristic have all yielded positive results

Self-supervised learning based knowledge distillation framework for automatic speech recognition for hearing impaired

The use of speech processing applications, particularly speech recognition, has got a lot of attention in recent decades. In recent years, research has focused on using deep learning for speech-related applications. This new branch of machine learning has outperformed others in a range of applications, including voice, and has thus become a particularly appealing research subject. Noise, speaker variability, language variability, vocabulary size, and domain remain one of the most significant research difficulties in speech recognition. We investigated on self-supervised algorithm for the unlabelled data. In recent years, these algorithms have progressed significantly, with their efficacy approaching and supervised pre-training alternatives across a variety of data modalities such as image and video. The purpose of this research is to develop powerful models for audio speech recognition that do not require human annotation. We accomplish this by distilling information from an automatic speech recognition (ASR) model that was trained on a large audio-only corpus. We integrate Connectionist Temporal Classification (CTC) loss, KL divergence loss in distillation technique. We demonstrate that distillation significantly speeds up training. We evaluate our model with evaluation metric Word Error Rate (WER)