Spoken Language Recognition Using CNN

Mukherjee, Shyamapada; Neeraj, Shivam; Astha, Gangwal; Khaitan, Lokesh; Das, Amlan

doi:10.1109/icit48102.2019.00013

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…They obtained an accuracy of 76 per cent and 73 per cent using SVM and decision tree classifiers, respectively, on 5 hours of training data. Mukherjee et al (2019) used CNNs for LID in German, Spanish, and English. They used filter banks to extract features from frequency domain representations of the signal.…”

Section: Related Workmentioning

confidence: 99%

Is Attention always needed? A case study on language identification from speech

Mandal,

Pal,

Dutta

et al. 2024

Nat. lang. processing

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Language identification (LID) is a crucial preliminary process in the field of Automatic Speech Recognition (ASR) that involves the identification of a spoken language from audio samples. Contemporary systems that can process speech in multiple languages require users to expressly designate one or more languages prior to utilisation. The LID task assumes a significant role in scenarios where ASR systems are unable to comprehend the spoken language in multilingual settings, leading to unsuccessful speech recognition outcomes. The present study introduces convolutional recurrent neural network (CRNN)-based LID, designed to operate on the mel-frequency cepstral coefficient (MFCC) characteristics of audio samples. Furthermore, we replicate certain state-of-the-art methodologies, specifically the convolutional neural network (CNN) and Attention-based convolutional recurrent neural network (CRNN with Attention), and conduct a comparative analysis with our CRNN-based approach. We conducted comprehensive evaluations on thirteen distinct Indian languages, and our model resulted in over 98 per cent classification accuracy. The LID model exhibits high-performance levels ranging from 97 per cent to 100 per cent for languages that are linguistically similar. The proposed LID model exhibits a high degree of extensibility to additional languages and demonstrates a strong resistance to noise, achieving 91.2 per cent accuracy in a noisy setting when applied to a European Language (EU) dataset.

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Section: Related Workmentioning

confidence: 99%

Is Attention always needed? A case study on language identification from speech

Mandal,

Pal,

Dutta

et al. 2024

Nat. lang. processing

View full text Add to dashboard Cite

show abstract

“…Many studies on language identification have been conducted, with various feature extraction and classification techniques being used. Several techniques are used to extract features from the audio data, including phone recognition followed by language modeling (PRLM) [5] and parallel phone recognition followed by language modeling (PPRLM) [5] for phonetic approach or perceptual linear prediction (PLP) [5], mel-frequency cepstral coefficient (MFCC) [6]- [8], i-vector [8], [9] and x-vector [10] for the acoustic approx neural networks [11], convolutional neural networks (CNN) [12], [13], logistic regression (LR) [8], PLDA [14], Gaussian mixture model (GMM) [15], [16], support vector machine [17], [18] are among techniques used to classify the language spoken.…”

Section: Introductionmentioning

confidence: 99%

Spoken language identification on 4 Indonesian local languages using deep learning

Wijonarko

Zahra

2022

Bulletin EEI

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Language identification is at the forefront of assistance in many applications, including multilingual speech systems, spoken language translation, multilingual speech recognition, and human-machine interaction via voice. The identification of indonesian local languages using spoken language identification technology has enormous potential to advance tourism potential and digital content in Indonesia. The goal of this study is to identify four Indonesian local languages: Javanese, Sundanese, Minangkabau, and Buginese, utilizing deep learning classification techniques such as artificial neural network (ANN), convolutional neural network (CNN), and long-term short memory (LSTM). The selected extraction feature for audio data extraction employs mel-frequency cepstral coefficient (MFCC). The results showed that the LSTM model had the highest accuracy for each speech duration (3 s, 10 s, and 30 s), followed by the CNN and ANN models.

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“…There are several steps to identify language, starting from cleaning the data from noise to help the system get better accuracy, extracting the feature from speech data, and classifying the language. There are several techniques to classify the language spoken, including neural networks [2], convolutional neural networks [3]- [6], logistic regression [7], PLDA [8], gaussian mixture model [9], [10], support vector machine [11], [12], and several techniques to extract the features from the recording, such as MFCC [13], [14], ivector [15]- [17], and x-vector [18].…”

Section: Introductionmentioning

confidence: 99%

Spoken language identification using i-vectors, x-vectors, PLDA and logistic regression

Abdurrahman

Zahra

2021

Bulletin EEI

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In this paper, i-vector and x-vector is used to extract the features from speech signal from local Indonesia languages, namely Javanese, Sundanese and Minang languages to help classifier identify the language spoken by the speaker. Probabilistic linear discriminant analysis (PLDA) are used as the baseline classifier and logistic regression technique are used because of prior studies showing logistic regression has better performance than PLDA for classifying speech data. Once these features are extracted. The feature is going to be classified using the classifier mentioned before. In the experiment, we tried to segment the test data to three segment such as 3, 10, and 30 seconds. This study is expanded by testing multiple parameters on the i-vector and x-vector method then comparing PLDA and logistic regression performance as its classifier. The x-vector has better score than i-vector for every segmented data while using PLDA as its classifier, except where the i-vector and x-vector is using logistic regression, i-vector still has better accuracy compared to x-vector.

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Spoken Language Recognition Using CNN

Cited by 7 publications

References 11 publications

Is Attention always needed? A case study on language identification from speech

Is Attention always needed? A case study on language identification from speech

Spoken language identification on 4 Indonesian local languages using deep learning

Spoken language identification using i-vectors, x-vectors, PLDA and logistic regression

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