Modelling multi-level prosody and spectral features using deep neural network for an automatic tonal and non-tonal pre-classification-based Indian language identification system

Bhanja, Chuya China; Laskar, Mohammad Azharuddin; Laskar, Rabul Hussain

doi:10.1007/s10579-020-09527-z

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…Artificial intelligence has achieved new goals in developing intelligent algorithms for processing languages (both text and audio), making humans more interactive with the systems. Many languages have been computationally modelled with a lot of effort [1][2][3]. In the early days of voice recognition and speaker recognition, speech signals were portrayed as a normal (smooth input without first-and second-order derivatives) input to systems.…”

Section: Introductionmentioning

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: Introductionmentioning

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 Review on Indian Language Identification Using Deep Learning

Sawalkar,

Roy

2024

Smart Innovation, Systems and Technologies

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Entropy-Argumentative Concept of Computational Phonetic Analysis of Speech Taking into Account Dialect and Individuality of Phonation

Kovtun

Семенов

2022

Entropy

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In this article, the concept (i.e., the mathematical model and methods) of computational phonetic analysis of speech with an analytical description of the phenomenon of phonetic fusion is proposed. In this concept, in contrast to the existing methods, the problem of multicriteria of the process of cognitive perception of speech by a person is strictly formally presented using the theoretical and analytical apparatus of information (entropy) theory, pattern recognition theory and acoustic theory of speech formation. The obtained concept allows for determining reliably the individual phonetic alphabet inherent in a person, taking into account their inherent dialect of speech and individual features of phonation, as well as detecting and correcting errors in the recognition of language units. The experiments prove the superiority of the proposed scientific result over such common Bayesian concepts of decision making using the Euclidean-type mismatch metric as a method of maximum likelihood and a method of an ideal observer. The analysis of the speech signal carried out in the metric based on the proposed concept allows, in particular, for establishing reliably the phonetic saturation of speech, which objectively characterizes the environment of speech signal propagation and its source.

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Modelling multi-level prosody and spectral features using deep neural network for an automatic tonal and non-tonal pre-classification-based Indian language identification system

Cited by 3 publications

References 33 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 Review on Indian Language Identification Using Deep Learning

Entropy-Argumentative Concept of Computational Phonetic Analysis of Speech Taking into Account Dialect and Individuality of Phonation

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