A CNN-BiLSTM based hybrid model for Indian language identification

Das, Himanish Shekhar; Roy, Pinki

doi:10.1016/j.apacoust.2021.108274

Cited by 15 publications

(5 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning architectures’ main contribution is their capacity to autonomously extract low-level to high-level properties ( Das and Roy, 2021 ). CNNs are the best models for identifying detailed properties in images.…”

Section: Methodsmentioning

confidence: 99%

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Das

Das²,

Neog³

et al. 2023

Front. Genet.

Self Cite

View full text Add to dashboard Cite

Introduction: Of all the cancers that afflict women, breast cancer (BC) has the second-highest mortality rate, and it is also believed to be the primary cause of the high death rate. Breast cancer is the most common cancer that affects women globally. There are two types of breast tumors: benign (less harmful and unlikely to become breast cancer) and malignant (which are very dangerous and might result in aberrant cells that could result in cancer).Methods: To find breast abnormalities like masses and micro-calcifications, competent and educated radiologists often examine mammographic images. This study focuses on computer-aided diagnosis to help radiologists make more precise diagnoses of breast cancer. This study aims to compare and examine the performance of the proposed shallow convolutional neural network architecture having different specifications against pre-trained deep convolutional neural network architectures trained on mammography images. Mammogram images are pre-processed in this study's initial attempt to carry out the automatic identification of BC. Thereafter, three different types of shallow convolutional neural networks with representational differences are then fed with the resulting data. In the second method, transfer learning via fine-tuning is used to feed the same collection of images into pre-trained convolutional neural networks VGG19, ResNet50, MobileNet-v2, Inception-v3, Xception, and Inception-ResNet-v2.Results: In our experiment with two datasets, the accuracy for the CBIS-DDSM and INbreast datasets are 80.4%, 89.2%, and 87.8%, 95.1% respectively.Discussion: It can be concluded from the experimental findings that the deep network-based approach with precise tuning outperforms all other state-of-the-art techniques in experiments on both datasets.

show abstract

Section: Methodsmentioning

confidence: 99%

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Das

Das²,

Neog³

et al. 2023

Front. Genet.

Self Cite

View full text Add to dashboard Cite

show abstract

“…7. The spectrogram representations of audio signals are suitable for performing audio classification using machine learning techniques [37].…”

Section: Speech Data Preparationmentioning

confidence: 99%

A Novel Convolutional Neural Network Model for Automatic Speaker Identification From Speech Signals

Pandian,

Thirunavukarasu,

Kotei

2024

IEEE Access

View full text Add to dashboard Cite

Since voice communication is the most frequent form of human communication, it is necessary to identify each speaker in a discussion. The development of advanced computer processing units, deep learning techniques and publicly available datasets prompt the need for proposing novel automated speaker recognition systems. This study presents a novel Convolutional Neural Network (CNN) called Five Convolutional Blocks-CNN (5C-CNN) for automatic speaker identification from speech signals. The proposed 5C-CNN model comprises five Convolutional blocks and one dense block for identifying the speaker using their English speech audio. The proposed framework was trained with English audio speech signals collected from Ten individuals. The study also applied different audio augmentation techniques like white Gaussian noise, shifting, stretching, stretching with high frequency, altering speed, and changing pitch values to introduce diversity in the dataset. HyperBand tuning technique was used to optimize the training parameters of the proposed approach. The proposed 5C-CNN achieved a classification accuracy of 99.34% on the test data of the proposed person speech dataset. Also, the proposed approach performed well when tested against a benchmark dataset namely THUYG-20 for better generalization with an accuracy of 95.43%. Both experimental investigations clearly confirm the efficiency of the proposed technique in the audio classification task while comparing the existing approaches, and standard machine learning techniques.

show abstract

“…The excellent performance of Bi-LSTM in machine translation makes many tasks try to use bidirectional LSTM. In Automatic language identification task [35], Bi-LSTM effectively extracts "future" speech sequences, and the effect is remarkable. In the sentiment analysis task [36], Bi-LSTM can effectively extract the context information and obtain more accurate prediction results.…”

Section: Related Workmentioning

confidence: 99%

Image Caption Generation Using Contextual Information Fusion With Bi-LSTM-s

Zhang

Jiang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

The image caption generation algorithm necessitates the expression of image content using accurate natural language. Given the existing encoder-decoder algorithm structure, the decoder solely generates words one by one in a front-to-back order and is unable to analyze integral contextual information. This paper employs a Bi-LSTM (Bi-directional Long Short-Term Memory) structure, which not only draws on past information but also captures subsequent information, resulting in the prediction of image content subject to the context clues. The visual information is respectively fed into the F-LSTM decoder (forward LSTM decoder) and B-LSTM decoder (backward LSTM decoder) to extract semantic information, along with complementing semantic output. Specifically, the subsidiary attention mechanism S-Att acts between F-LSTM and B-LSTM, while the semantic information of B-LSTM and F-LSTM is extracted using the attention mechanism. Meanwhile, the semantic interaction is extracted pursuant to the similarity while aligning the hidden states, resulting in the output of the fused semantic information. We adopt a Bi-LSTM-s model capable of extracting contextual information and realizing finer-grained image captioning effectively. In the end, our model improved by 9.7% on the basis of the original LSTM. In addition，our model effectively solves the problem of inconsistent semantic information in the forward and backward direction of the simultaneous order, and gets a score of 37.5 on BLEU-4. The superiority of this approach is experimentally demonstrated on the MSCOCO dataset.

show abstract

A CNN-BiLSTM based hybrid model for Indian language identification

Cited by 15 publications

References 40 publications

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach

A Novel Convolutional Neural Network Model for Automatic Speaker Identification From Speech Signals

Image Caption Generation Using Contextual Information Fusion With Bi-LSTM-s

Contact Info

Product

Resources

About