Speaker Gender Classification in Mono-Language and Cross-Language Using BLSTM Network

Alashban, Adal A.; Alotaibi, Yousef Ajami

doi:10.1109/tsp52935.2021.9522623

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…Given the large feature dimensions, it is advisable to combine learning models as suggested in [9,13]. Additionally, training the model with multiple languages [28] can further enrich the information and broaden its applicability.…”

Section: Discussionmentioning

confidence: 99%

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A Robust Gender Recognition System using Convolutional Neural Network on Indonesian Speaker

Switrayana,

Hadi,

Sulistianingsih

2024

SISTEMASI

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Voice is one of the biometrics that humans have. Humans can be recognized by the sounds produced by their vocal cords and vocal tracts. One of the uses of voice is to recognize gender. Despite extensive research, gender recognition using machine learning remains unsatisfactory due to the complexity of voice features and the limitations of conventional algorithms. In this research, voice-based gender recognition is performed by applying deep learning. The deep learning model used is the Convolutional Neural Network (CNN). The input of CNN is the result of feature extraction from the Mel-Frequency Cepstral Coefficients (MFCC) method. MFCC produces Mel-Spectograms which are important features of sound. The dataset used is Indonesian speech. In the research, there are imbalanced and balanced dataset scenarios to see the performance of the model. To produce a balanced dataset, random undersampling is performed on the majority class. In addition, the effect of dividing training and testing data with a composition of 70:30, 80:20, and 90:10 was observed. The results show that the model has 100% accuracy for all imbalanced dataset scenarios. Then the highest accuracy is 99.65% for the balanced dataset scenario with 70:30 splitting. In summary, it can be concluded that CNN performs very well in identifying gender from voice features overall, although its performance decreases when random undersampling is applied to the dataset.

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Section: Discussionmentioning

confidence: 99%

“…This stage involves signal processing techniques. There are several feature extraction techniques including Gammatone Cepstral Coefficients (GTCC), Spectral Entropy, Harmonic Ratio, Mel-Frequency Cepstral Coefficients (MFCC), and others [28]. Features that can also be extracted in signals are acoustic features [7,13].…”

Section: Feature Extractionmentioning

confidence: 99%

A Robust Gender Recognition System using Convolutional Neural Network on Indonesian Speaker

Switrayana,

Hadi,

Sulistianingsih

2024

SISTEMASI

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“…Researchers can use this corpus to train, validate, and test speech-enabled systems such as speech recognition systems and other digital speech processing tasks. Additionally, it may benefit different fields, such as gender classification [36], [37], speaker identification, speech recognition, or environment recognition and classification. Each row in the file (.TSV) represents a single speech file and contains the ID, sentence, gender, accent, age, and path.…”

Section: Selected Speech Corpusmentioning

confidence: 99%

A Deep Learning Approach for Identifying and Discriminating Spoken Arabic Among Other Languages

Alashban

Alotaibi

2023

IEEE Access

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Spoken Language Identification (SLID) is an important step in speech-to-speech translation systems and multi-lingual automatic speech recognition. In recent research, deep learning mechanisms have been the prevailing approaches for spoken language identification. This paper aims to study, detect, and analyze spoken languages similar to Arabic in pronouncing certain words and then proposes a deep learningbased architecture, specifically the Bidirectional Long Short Term Memory (BLSTM), for spoken Arabic language identification and discrimination between these similar languages, namely, German, Spanish, French, and Russian, all of which are taken from Mozilla speech corpus languages. Additionally, our work involves a linguistic study of these considered languages. A total of ten thousand speakers are chosen for all five languages, and the BLSTM architecture is designed and implemented using acoustic signal features and applied to five experiments in this paper. The results show a precision of 98.97%, 98.73%, 98.47%, and 99.75% for identifying the spoken Arabic language separately along with German, Spanish, French, and Russian, respectively. Additionally, we achieved an average accuracy of 95.15% for discriminating between all these considered five languages in terms of the pronunciation of words. Our findings confirm that a BLSTM architecture is able to distinguish between observable similar pronunciations of words in considered languages.

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“…Thus, the loss is not a percentage. Instead, it is a summation of the errors made for each example in training or validation sets [41].…”

Section: ) Loss Ratementioning

confidence: 99%

Single Convolutional Neural Network With Three Layers Model for Crowd Density Estimation

et al. 2022

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Crowd density estimation is an important topic in computer vision due to its widespread applications in surveillance, urban planning, and intelligence gathering. Resulting from extensive analysis, crowd density estimation reflects many aspects such as similarity of appearance between people, background components, and inter-blocking in intense crowds. In this paper, we are interested to apply machine learning for crowd management in order to monitor populated area and prevent congestion situations. We propose a Single-Convolutional Neural Network with Three Layers (S-CNN3) model to count the number of people in a scene and conclude about the crowd estimation. Then, a comparative study for density counting establishes the performance of the proposed model against the convolutional neural networks with four layers (single-CNN4) and Switched Convolutional neural networks (SCNN). ShanghaiTech dataset, considered as the largest data base for crowd counting, is used in this work. The proposed model proves high effectiveness and efficiency for crowd density estimation with 99.88% of average test accuracy and 0.02 of average validation loss. These results achieve better performance than the existing state-of-the-art models.

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Speaker Gender Classification in Mono-Language and Cross-Language Using BLSTM Network

Cited by 9 publications

References 8 publications

A Robust Gender Recognition System using Convolutional Neural Network on Indonesian Speaker

A Robust Gender Recognition System using Convolutional Neural Network on Indonesian Speaker

A Deep Learning Approach for Identifying and Discriminating Spoken Arabic Among Other Languages

Single Convolutional Neural Network With Three Layers Model for Crowd Density Estimation

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