Bangla Speech Recognition System Using LPC and ANN

Paul, Anup Kumar; Das, Dipankar; Kamal, Md. Mustafa

doi:10.1109/icapr.2009.80

Cited by 72 publications

(17 citation statements)

References 2 publications

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“…Recognition of the syllables among the smaller number of cohorts within a class is done by means of hidden Markov models. [62]. The self-organizing map (SOM) structure of ANN makes each variable length LPC trajectory of an isolated word into a fixed length LPC trajectory and thereby making the fixed length feature vector to be fed into the recognizer.…”

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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“…The experimental results showed good precisions. The Bangla speech recognition system was presented in [28]. The feature extractor used a standard LPC cepstrum coder and pattern recognition was done by using artificial neural network (ANN) which provided better results.…”

Section: Linear Predictive Coding (Lpc)mentioning

confidence: 99%

Feature Extraction Techniques in Speech Processing: A Survey

Hibare¹,

Vibhute²

2014

IJCA

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Speech processing includes the various techniques such as speech coding, speech synthesis, speech recognition and speaker recognition. In the area of digital signal processing, speech processing has versatile applications so it is still an intensive field of research. Speech processing mostly performs two fundamental operations such as Feature Extraction and Classification. The main criterion for the good speech processing system is the selection of feature extraction technique which plays an important role in the system accuracy. This paper intends to focus on the survey of various feature extraction techniques in speech processing such as Fast Fourier Transforms, Linear Predictive Coding, Mel Frequency Cepstral Coefficients, Discrete Wavelet Transforms, Wavelet Packet Transforms, Hybrid Algorithm DWPD and their applications in speech processing.

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“…Main drawback is that vocabulary size is too small. Anup Kumar Paul, Dipankar Das et al [25] developed Bangla Speech Recognition System using LPC andANN (automatic neural network). This paper presents recognition system of the Bangla speech.…”

Section: Survey Of Hindi Speech Recognitionmentioning

confidence: 99%

Review of Development of Speech corpora and speech recognition research in Hindi

Petkar¹

2017

IJERA

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Communication of information and interaction is advocating very progressive changes due to technological elevation spoken language is not only natural but also faster way of communication among human being. Speech recognition and natural language understanding is bottleneck in realization of conclusive man-machine interface speech recognition along with the wide applicability in domain of computer science, medical science, psychology sports, neurology linguistics has increased demand for speech and text data. This speech corpora is utilized for the development of acoustic and language models which can be used for training automatic speech recognition, synthesis or translation system applicability of automatic speech recognition is amplified and reach to the masses if only developed in native languages. Hindi is one of the most widely spoken languages in the world and is major language in India. Manifold expression of Hindi language and its utility deviates the scientist towards thought-provoking research. This paper review the speech corpora development developed by different agencies and researcher speech recognition research in Hindi languages. In this paper 26 speech corpora is surveyed and their status and utility is observed also Hindi speech recognition system are review which is developed by 20 different researchers.

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Bangla Speech Recognition System Using LPC and ANN

Cited by 72 publications

References 2 publications

Speech Recognition in Indian Languages—A Survey

Speech Recognition in Indian Languages—A Survey

Feature Extraction Techniques in Speech Processing: A Survey

Review of Development of Speech corpora and speech recognition research in Hindi

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