Dataset Information

BdSL47: A complete depth-based Bangla sign alphabet and digit dataset.

ABSTRACT: Sign Language Recognition (SLR) is crucial for enabling communication between the deaf-mute and hearing communities. Nevertheless, the development of a comprehensive sign language dataset is a challenging task due to the complexity and variations in hand gestures. This challenge is particularly evident in the case of Bangla Sign Language (BdSL), where the limited availability of depth datasets impedes accurate recognition. To address this issue, we propose BdSL47, an open-access depth dataset for 47 one-handed static signs (10 digits, from ০ to ৯; and 37 letters, from অ to ँ) of BdSL. The dataset was created using the MediaPipe framework for extracting depth information. To classify the signs, we developed an Artificial Neural Network (ANN) model with a 63-node input layer, a 47-node output layer, and 4 hidden layers that included dropout in the last two hidden layers, an Adam optimizer, and a ReLU activation function. Based on the selected hyperparameters, the proposed ANN model effectively learns the spatial relationships and patterns from the depth-based gestural input features and gives an F1 score of 97.84 %, indicating the effectiveness of the approach compared to the baselines provided. The availability of BdSL47 as a comprehensive dataset can have an impact on improving the accuracy of SLR for BdSL using more advanced deep-learning models.

SUBMITTER: Rayeed SM

PROVIDER: S-EPMC10700367 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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BdSL47: A complete depth-based Bangla sign alphabet and digit dataset.

Rayeed S M SM Tuba Sidratul Tamzida ST Mahmud Hasan H Mazumder Mumtahin Habib Ullah MHU Mukta Saddam Hossain SH Hasan Kamrul K

Data in brief 20231111

Sign Language Recognition (SLR) is crucial for enabling communication between the deaf-mute and hearing communities. Nevertheless, the development of a comprehensive sign language dataset is a challenging task due to the complexity and variations in hand gestures. This challenge is particularly evident in the case of Bangla Sign Language (BdSL), where the limited availability of depth datasets impedes accurate recognition. To address this issue, we propose BdSL47, an open-access depth dataset fo ...[more]

PMID: 38075615

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Project description:The speech emotion recognition system determines a speaker's emotional state by analyzing his/her speech audio signal. It is an essential at the same time a challenging task in human-computer interaction systems and is one of the most demanding areas of research using artificial intelligence and deep machine learning architectures. Despite being the world's seventh most widely spoken language, Bangla is still classified as one of the low-resource languages for speech emotion recognition tasks because of inadequate availability of data. There is an apparent lack of speech emotion recognition dataset to perform this type of research in Bangla language. This article presents a Bangla language-based emotional speech-audio recognition dataset to address this problem. BanglaSER is a Bangla language-based speech emotion recognition dataset. It consists of speech-audio data of 34 participating speakers from diverse age groups between 19 and 47 years, with a balanced 17 male and 17 female nonprofessional participating actors. This dataset contains 1467 Bangla speech-audio recordings of five rudimentary human emotional states, namely angry, happy, neutral, sad, and surprise. Three trials are conducted for each emotional state. Hence, the total number of recordings involves 3 statements × 3 repetitions × 4 emotional states (angry, happy, sad, and surprise) × 34 participating speakers = 1224 recordings + 3 statements × 3 repetitions × 1 emotional state (neutral) × 27 participating speakers = 243 recordings, resulting in a total number of recordings of 1467. BanglaSER dataset is created by recording speech-audios through smartphones, and laptops, having a balanced number of recordings in each category with evenly distributed participating male and female actors, and would serve as an essential training dataset for the Bangla speech emotion recognition model in terms of generalization. BanglaSER is compatible with various deep learning architectures such as Convolutional neural networks, Long short-term memory, Gated recurrent unit, Transformer, etc. The dataset is available at https://data.mendeley.com/datasets/t9h6p943xy/5 and can be used for research purposes.

Project description:Speech Emotion Recognition (SER) identifies and categorizes emotional states by analyzing speech signals. SER is an emerging research area using machine learning and deep learning techniques due to its socio-cultural and business importance. An appropriate dataset is an important resource for SER related studies in a particular language. There is an apparent lack of SER datasets in Bangla language although it is one of the most spoken languages in the world. There are a few Bangla SER datasets but those consist of only a few dialogs with a minimal number of actors making them unsuitable for real-world applications. Moreover, the existing datasets do not consider the intensity level of emotions. The intensity of a specific emotional expression, such as anger or sadness, plays a crucial role in social behavior. Therefore, a realistic Bangla speech dataset is developed in this study which is called KUET Bangla Emotional Speech (KBES) dataset. The dataset consists of 900 audio signals (i.e., speech dialogs) from 35 actors (20 females and 15 males) with diverse age ranges. Source of the speech dialogs are Bangla Telefilm, Drama, TV Series, Web Series. There are five emotional categories: Neutral, Happy, Sad, Angry, and Disgust. Except Neutral, samples of a particular emotion are divided into two intensity levels: Low and High. The significant issue of the dataset is that the speech dialogs are almost unique with relatively large number of actors; whereas, existing datasets (such as SUBESCO and BanglaSER) contain samples with repeatedly spoken of a few pre-defined dialogs by a few actors/research volunteers in the laboratory environment. Finally, the KBES dataset is exposed as a nine-class problem to classify emotions into nine categories: Neutral, Happy (Low), Happy (High), Sad (Low), Sad (High), Angry (Low), Angry (High), Disgust (Low) and Disgust (High). However, the dataset is kept symmetrical containing 100 samples for each of the nine classes; 100 samples are also gender balanced with 50 samples for male/female actors. The developed dataset seems a realistic dataset while compared with the existing SER datasets.

Dataset Information

BdSL47: A complete depth-based Bangla sign alphabet and digit dataset.

Publications

BdSL47: A complete depth-based Bangla sign alphabet and digit dataset.

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