An ANN based approach to recognize initial phonemes of spoken words of Assamese language

Sarma, Mousmita; Sarma, Kandarpa Kumar

doi:10.1016/j.asoc.2013.01.004

Cited by 19 publications

(6 citation statements)

References 4 publications

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“…15. Sarma and Sarma in 2013, reported a work [73] where a hybrid ANN model is used to recognize initial phones from CVC-type Assamese words. An SOMbased algorithm is developed to segment the initial phonemes from its word counterpart.…”

Section: Sarkar and Yegnanarayana Have Used Fuzzy Rough Neural Networmentioning

confidence: 99%

Speech Recognition in Indian Languages—A Survey

Sarma

2015

Recent Trends in Intelligent and Emerging Systems

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In this paper, a brief overview derived out of detailed survey of speech recognition works reported in Indian languages is described. Robustness of speech recognition systems toward language variation is the recent trend of research in speech recognition technology. To develop a system which can communicate with human in any language like any other human is the foremost requirement in order to design appropriate speech recognition technology for one to all. India is a country which has vast linguistic variations among its billion plus population. Therefore, it provides a sound area of research toward language-specific speech recognition technology. From the beginning of the commercial availability of the speech recognition system, the technology has been dominated by the hidden Markov model (HMM) methodology due to its capability of modeling temporal structures of speech and encoding them as a sequence of spectral vectors. Most of the work done in Indian languages also uses HMM technology. However, from the last 10-15 years after the acceptance of neurocomputing as an alternative to HMM, artificial neural network (ANN)-based methodologies have started to receive attention for application in speech recognition. This is a trend worldwide as part of which few works have also been reported by a few researchers.

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Section: Sarkar and Yegnanarayana Have Used Fuzzy Rough Neural Networmentioning

confidence: 99%

Speech Recognition in Indian Languages—A Survey

Sarma

2015

Recent Trends in Intelligent and Emerging Systems

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“…Marathi [8,10,12,13,15,16,18,20,26,63,78] Indo-Aryan Gujarati [8, 16-21, 64, 78] Indo-Aryan Hindi [9, 15, 22-24, 26, 27, 27, 60, 61, 70, 71] Indo-Aryan Bengali [6, 9, 26, 28-30, 32, 62, 71] Indo-Aryan Assamese [21,[33][34][35][36][37] the other channel was a head set microphone, where data were recorded at 48 kHz sampling frequency and 16 bits/sample resolution. It was transcribed using the IPA symbols.…”

Section: Languagementioning

confidence: 99%

Spoken Indian language identification: a review of features and databases

Bakshi

Kopparapu

2018

Sādhanā

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Spoken language is one of the distinctive characteristics of the human race. Spoken language processing is a branch of computer science that plays an important role in human-computer interaction (HCI), which has made remarkable advancement in the last two decades. This paper reviews and summarizes the acoustic, phonetic and prosody features that have been used for spoken language identification specifically for Indian languages. In addition, we also review the speech databases, which are already available for Indian languages and can be used for the purposes of spoken language identification.

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“…After referring to many references, a lot of studies have shown the neural network is an important component of a complicated nonlinear system control policy under the circumstance of the lack of full model details [5][6][7][8]. e most prominent advantage of the neural network is approximate capability, and it can approximate function with any precision.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Wavelet Neural Network with the Improved Levenberg–Marquardt Algorithm for the AC Servo System

et al. 2021

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In this study, a fuzzy wavelet neural network with the improved Levenberg–Marquardt algorithm (FWNN-LM) is proposed to conquer nonlinearity and uncertain disturbance problems in the AC servo system. First of all, use the particle swarm optimization algorithm based on Levenberg–Marquardt (LM) to optimize parameters in the FWNN controller. Second, the potentiality of fuzzy rules (PFR) method is developed to optimize the structure of the FWNN by error reduction ratio (ERR). Furthermore, stability of FWNN-LM is proved by the Lyapunov method. Finally, simulation and prototype test results show that this method can improve the accuracy and robustness of the system in presence of load disturbances and parameter perturbations.

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An ANN based approach to recognize initial phonemes of spoken words of Assamese language

Cited by 19 publications

References 4 publications

Speech Recognition in Indian Languages—A Survey

Speech Recognition in Indian Languages—A Survey

Spoken Indian language identification: a review of features and databases

Fuzzy Wavelet Neural Network with the Improved Levenberg–Marquardt Algorithm for the AC Servo System

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