Language Effect on Speaker Gender Classification Using Deep Learning

Alashban, Adal A.; Alotaibi, Yousef Ajami

doi:10.1109/aisp53593.2022.9760599

Cited by 2 publications

(1 citation statement)

References 19 publications

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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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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 System Using Convolutional Recurrent Neural Network

et al. 2022

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Following recent advancements in deep learning and artificial intelligence, spoken language identification applications are playing an increasingly significant role in our day-to-day lives, especially in the domain of multi-lingual speech recognition. In this article, we propose a spoken language identification system that depends on the sequence of feature vectors. The proposed system uses a hybrid Convolutional Recurrent Neural Network (CRNN), which combines a Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN) network, for spoken language identification on seven languages, including Arabic, chosen from subsets of the Mozilla Common Voice (MCV) corpus. The proposed system exploits the advantages of both CNN and RNN architectures to construct the CRNN architecture. At the feature extraction stage, it compares the Gammatone Cepstral Coefficient (GTCC) feature and Mel Frequency Cepstral Coefficient (MFCC) feature, as well as a combination of both. Finally, the speech signals were represented as frames and used as the input for the CRNN architecture. After conducting experiments, the results of the proposed system indicate higher performance with combined GTCC and MFCC features compared to GTCC or MFCC features used individually. The average accuracy of the proposed system was 92.81% in the best experiment for spoken language identification. Furthermore, the system can learn language-specific patterns in various filter size representations of speech files.

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Language Effect on Speaker Gender Classification Using Deep Learning

Cited by 2 publications

References 19 publications

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

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

Spoken Language Identification System Using Convolutional Recurrent Neural Network

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