Marathi Language Speech Synthesizer Using Concatenative Synthesis Strategy (Spoken in Maharashtra, India)

Shirbahadurkar, S. D.; Bormane, D. S.

doi:10.1109/icmv.2009.52

Cited by 10 publications

(7 citation statements)

References 5 publications

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“…However, the dependence of human computer interaction on written text and images makes the use of computers impossible for visually and physically impaired and illiterate masses [1]. Text-to-speech synthesis (TTS) helps speech processing researchers to act upon this problem by synthesizing speech (in local languages e.g.…”

Section: Introductionmentioning

confidence: 99%

Punjabi Speech Synthesis System using HTK

Bansal¹,

Goel²,

Jindal

2012

IJIST

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Section: Introductionmentioning

confidence: 99%

Punjabi Speech Synthesis System using HTK

Bansal¹,

Goel²,

Jindal

2012

IJIST

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“…In various regions of India, persons have shaken off the heavy features. Especially, English language can be spoken in spite of the regional languages [8]. The good speakers or bad speakers of English can be recognized in all over India.…”

Section: Basic Of Marathi Hindi and Englishmentioning

confidence: 99%

“…The array of sampled data is converted back into the speech signal [8]. A process is to determine the pronunciation of a numeric form based on its spelling which is often called text-tophoneme.…”

Section: A Numeric Text Forms and Speech Selection Based On Hybrid Tmentioning

confidence: 99%

“…Alternatively, the synthesizer can incorporate a model of the vocal tract. In an efficient model, the characteristics of person's voice are to produce a completely -synthetic/artificial‖ voice output [8]. The speech signals are normalized through the PRAAT tool which is used for speech analysis and developed by Paul Boersma and David Weenink, University of Amsterdam.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Model for Numerical Text-To-Speech Synthesis System in Marathi, Hindi and English Languages

Ramteke¹,

Ramteke²

2017

IJIGSP

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Abstract-The paper proposes a numerical TTSsynthesis system in Marathi, Hindi and English languages. The system is in audible forms based on the sounds generated from several numeric units. A hybrid technique is newly launched for a numerical text-to-speech technology. The technique is divided into different phases. These numerical phases include pre-processing, numeric unit detection, numeric and speech unit matching; speech unit concatenation and speech generation. In order to enhance the syntactic generation of audible forms in three languages, two discipline tests were performed. The prosodic test has been obtained for evaluating on the statistical readings. Overall quality issue (OQI) test is a subjective test which is performed by various persons who are aware of three mentioned languages. On the basis of two distinct parameters of OQI test, all scores are positively provided. Initial parameter compromises with listening quality. The second parameter, awareness rate improves a level of the intelligibility. The ultimate satisfactory results of artificial sound generation in three unrelated languages were touched to humankind voice.

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“…Speech is one of the salient forms of communication in daily life (Shirbahadurkar and Bormane, 2009). Speech is formed through the functioning of time-varying vocal tract system.…”

Section: Introductionmentioning

confidence: 99%

An Effective System for Acute Spotting Aberration in the Speech of Abnormal Children Via Artificial Neural Network and Genetic Algorithm

Bharathi¹,

Shanthi²

2012

American Journal of Applied Sciences

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Problem statement:In real-world environment, speech signal processing plays a vital role among the research communities. A wide range of researches are carried out in this field for denoising, enhancement and more. Besides the other, stress management is important to identify the spot in which the stress has to be made. Approach: In this study, in order to provide proper speech practice for the abnormal person, their speech is analyzed. Initially, the normal and abnormal person's speech are obtained with the same set of words. As an initial process, the Mel Frequency Cepstrum Coefficients (MFCC) is extracted from both words and the Principal Component Analysis (PCA) is applied to reduce the dimensionality of the words. From the dimensionality reduced words, the parameters are obtained and then these parameters are utilized to train the ANN which is used to identify the word that is abnormal. After identifying the abnormal word, the acute word is extracted through the thresholding operation and then FFT is computed for the acute word. From this FFT, the parameters are obtained and then these parameters are used in the genetic algorithm for optimization. GA is used to identify the spot in which the speech practice is required for the abnormal person. Results: The proposed system is implemented in the working platform of MATLAB. The performance of the proposed system is tested by generating the dataset for the normal and abnormal female children. Conclusion: In this study, an effective system has been proposed to identify the abnormal word and the spot in which the speech has to be improved also identified.

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Marathi Language Speech Synthesizer Using Concatenative Synthesis Strategy (Spoken in Maharashtra, India)

Cited by 10 publications

References 5 publications

Punjabi Speech Synthesis System using HTK

Punjabi Speech Synthesis System using HTK

Efficient Model for Numerical Text-To-Speech Synthesis System in Marathi, Hindi and English Languages

An Effective System for Acute Spotting Aberration in the Speech of Abnormal Children Via Artificial Neural Network and Genetic Algorithm

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