A survey on speech synthesis techniques in Indian languages

Panda, Soumya Priyadarsini; Nayak, Ajit Kumar; Champati, Satyananda

doi:10.1007/s00530-020-00659-4

Cited by 21 publications

(13 citation statements)

References 144 publications

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“…The highest separation is achieved for all the classes from a-n. Similarly, for Category-B (n-z) we have obtained the highest separation for all the classes except for class (15) σ ′ j = ∂j ∂s i j "o," i.e. AUC (0.98) and "x," i.e.…”

Section: Resultsmentioning

confidence: 63%

“…Various studies have been conducted to convert Braille into a natural language. Applications such as BeMyEyes [10], BeSpecular [11], TapTapSee [12], KNFBReader [13], Color Teller [14], Text to Speech Synthesizer [15][16][17] have been specifically developed to improve the quality of life for people with visual impairments. Most of these take input from scanned Braille documents using computational tools.…”

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confidence: 99%

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Deep learning scheme for character prediction with position-free touch screen-based Braille input method

Shokat

Riaz

Rizvi

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Smart devices are effective in helping people with impairments, overcome their disabilities, and improve their living standards. Braille is a popular method used for communication by visually impaired people. Touch screen smart devices can be used to take Braille input and instantaneously convert it into a natural language. Most of these schemes require location-specific input that is difficult for visually impaired users. In this study, a position-free accessible touchscreen-based Braille input algorithm is designed and implemented for visually impaired people. It aims to place the least burden on the user, who is only required to tap those dots that are needed for a specific character. The user has input English Braille Grade 1 data (a–z) using a newly designed application. A total dataset comprised of 1258 images was collected. The classification was performed using deep learning techniques, out of which 70%–30% was used for training and validation purposes. The proposed method was thoroughly evaluated on a dataset collected from visually impaired people using Deep Learning (DL) techniques. The results obtained from deep learning techniques are compared with classical machine learning techniques like Naïve Bayes (NB), Decision Trees (DT), SVM, and KNN. We divided the multi-class into two categories, i.e., Category-A (a–m) and Category-B (n–z). The performance was evaluated using Sensitivity, Specificity, Positive Predicted Value (PPV), Negative Predicted Value (NPV), False Positive Rate (FPV), Total Accuracy (TA), and Area under the Curve (AUC). GoogLeNet Model, followed by the Sequential model, SVM, DT, KNN, and NB achieved the highest performance. The results prove that the proposed Braille input method for touch screen devices is more effective and that the deep learning method can predict the user's input with high accuracy.

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Section: Resultsmentioning

confidence: 63%

mentioning

confidence: 99%

Deep learning scheme for character prediction with position-free touch screen-based Braille input method

Shokat

Riaz

Rizvi

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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“…If these smart devices can be operated by speech commands, especially in the local languages, the entire population of India can use them with ease. For that purpose, researchers are trying to develop various speech processing applications, such as automatic speech recognition [54,72,88,167], speech synthesis [12,133], speaker recognition [71], etc. for individual Indian languages.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

Dey

Sahidullah

Saha

2022

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum in the last two decades, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields.

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“…India is home to over 19,565 mother tongues/dialects, according to 2011 Census. Every language has its own phonotactic, prosodic, and acoustic characteristics [5]. As a result, identifying these languages, each with its own vernacular, cadence, semantics, and ambiance, becomes exceedingly difficult [6][7] [8].…”

Section: Introductionmentioning

confidence: 99%

G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language Using Cat Boost

Gupta

Singh²,

Singh

2022

Wireless Pers Commun

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The pandemic caused due to COVID-19, has seen things going online. People tired of typing prefer to give voice commands. Most of the voice based applications and devices are not prepared to handle the native languages. Moreover, in a party environment it is difficult to identify a voice command as there are many speakers. The proposed work addresses the Cocktail party problem of Indian language, Gujarati. The voice response systems like, Siri, Alexa, Google Assistant as of now work on single voice command. The proposed algorithm G-Cocktail would help these applications to identify command given in Gujarati even from a mixed voice signal. Benchmark Dataset is taken from Microsoft and Linguistic Data Consortium for Indian Languages(LDC-IL) comprising single words and phrases. G-Cocktail utilizes the power of CatBoost algorithm to classify and identify the voice. Voice print of the entire sound files is created using Pitch, and Mel Frequency Cepstral Coefficients (MFCC). Seventy percent of the voice prints are used to train the network and thirty percent for testing. The proposed work is tested and compared with K-means, Naïve Bayes, and LightGBM.

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A survey on speech synthesis techniques in Indian languages

Cited by 21 publications

References 144 publications

Deep learning scheme for character prediction with position-free touch screen-based Braille input method

Deep learning scheme for character prediction with position-free touch screen-based Braille input method

An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language Using Cat Boost

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