Understanding Records. A Field Guide to Recording Practice. Second Edition. By Jay Hodgson. New York: Bloomsbury, 2019. 233 pp. ISBN 978-1-5013-4237-0

Keeken, Alan van

doi:10.1017/s0261143021000192

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Figure 1 depicts the basic architecture of our model, consisting of mainly three components, i.e., pre-processing, feature extraction, and classification. Pre-processing is done to remove high noise and silence using the Spectral Noise Gate (SNG) [37]. The pre-processed signal is then forwarded for feature extraction (using MFCC, RASTA-PLP, and spectral features), and at the end, RNN-LSTM classifiers are used for classification.…”

Section: Proposed Modelmentioning

confidence: 99%

A comparison of cepstral and spectral features using recurrent neural network for spoken language identification

Thukroo,

Bashir,

Giri

2024

Comput. Artif. Intell.

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Spoken language identification is the process of confirming labels regarding the language of an audio slice regardless of various features such as length, ambiance, duration, topic or message, age, gender, region, emotions, etc. Language identification systems are of great significance in the domain of natural language processing, more specifically multi-lingual machine translation, language recognition, and automatic routing of voice calls to particular nodes speaking or knowing a particular language. In his paper, we are comparing results based on various cepstral and spectral feature techniques such as Mel-frequency Cepstral Coefficients (MFCC), Relative spectral-perceptual linear prediction coefficients (RASTA-PLP), and spectral features (roll-off, flatness, centroid, bandwidth, and contrast) in the process of spoken language identification using Recurrent Neural Network-Long Short Term Memory (RNN-LSTM) as a procedure of sequence learning. The system or model has been implemented in six different languages, which contain Ladakhi and the five official languages of Jammu and Kashmir (Union Territory). The dataset used in experimentation consists of TV audio recordings for Kashmiri, Urdu, Dogri, and Ladakhi languages. It also consists of standard corpora IIIT-H and VoxForge containing English and Hindi audio data. Pre-processing of the dataset is done by slicing different types of noise with the use of the Spectral Noise Gate (SNG) and then slicing into audio bursts of 5 seconds duration. The performance is evaluated using standard metrics like F1 score, recall, precision, and accuracy. The experimental results showed that using spectral features, MFCC and RASTA-PLP achieved an average accuracy of 76%, 83%, and 78%, respectively. Therefore, MFCC proved to be the most convenient feature to be exploited in language identification using a recurrent neural network long short-term memory classifier.

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Section: Proposed Modelmentioning

confidence: 99%

A comparison of cepstral and spectral features using recurrent neural network for spoken language identification

Thukroo,

Bashir,

Giri

2024

Comput. Artif. Intell.

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A Hybrid Convolutional Bi-Directional Gated Recurrent Unit System for Spoken Languages of JK and Ladakhi

Thukroo

Bashir

Giri

2023

J. Info. Know. Mgmt.

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Spoken language identification is the process of recognising language in an audio segment and is the precursor for several technologies such as automatic call routing, language recognition, multilingual conversation, language parsing, and sentimental analysis. Language identification has become a challenging task for low-resource languages like Kashmiri and Ladakhi spoken in the UT’s of Jammu and Kashmir (JK) and Ladakh, India. This is mainly due to speaker variations like duration, moderator, and ambiance particularly when training and testing are done on different datasets whilst analysing the accuracy of language identification system in actual implementation, thus producing low accuracy results. In order to tackle this problem, we propose a hybrid convolutional bi-directional gated recurrent unit (Bi-GRU) utilising the effects of both static and dynamic behaviour of the audio signal in order to achieve better results as compared to state-of-the-art models. The audio signals are first converted into two-dimensional structures called Mel-spectrograms to represent the frequency distribution over time. To investigate the spectral behaviour of audio signals, we employ a convolutional neural network (CNN) that perceives Mel-spectrograms in multiple dimensions. The CNN-learned feature vector serves as input to the Bi-GRU that maintains the dynamic behaviour of the audio signal. Experiments are done on six spoken languages, i.e. Ladakhi, Kashmiri, Hindi, Urdu, English, and Dogri. The data corpora used for experimentation are the International Institute of Information Technology Hyderabad-Indian Language Speech Corpus (IIITH-ILSC) and the self-created data corpus for the Ladakhi language. The model is tested on two datasets, i.e. speaker-dependent and speaker-independent. Results show that when validating the efficiency of our proposed model on both speaker-dependent and speaker-independent datasets, we achieve optimal accuracies of 99% and 91%, respectively, thus achieving promising results in comparison to the state-of-the-art models available.

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Understanding Records. A Field Guide to Recording Practice. Second Edition. By Jay Hodgson. New York: Bloomsbury, 2019. 233 pp. ISBN 978-1-5013-4237-0

Cited by 2 publications

References 4 publications

A comparison of cepstral and spectral features using recurrent neural network for spoken language identification

A comparison of cepstral and spectral features using recurrent neural network for spoken language identification

A Hybrid Convolutional Bi-Directional Gated Recurrent Unit System for Spoken Languages of JK and Ladakhi

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