An efficient adaptive artificial neural network based text to speech synthesizer for Hindi language

Speech recognition is one of the major research regions these days under speech processing. This paper depends on developing a whole process that takes the input as the text file from the user and provides the output in speech form. This paper proposes a text to speech synthesizer for the Hindi language depends on the coefficients of Mel-frequency cepstral (MFCC) features are extracted to the production and linguistic constraints proposed for modeling the parameters such as intonation, duration, and syllable intensities. The features extracted from the MFCC features are phrasing, fundamental frequency, duration, etc. Neural network models are discovered to confine the features as mentioned earlier, employing MFCC. The performance of the proposed ALO-ANN is computed utilizing objective measures such as prediction error (η), standard deviation (σ), and linear correlation coefficient (χ). The accuracy predicted of the proposed ALO-ANN models is high when compared with other models such as DNN and ANN. The prediction accuracy is high for ALO-ANN models when compared with other models.