Performance Analysis of Text To Speech Synthesis System Using HMM And Prosody Features With Parsing For Tamil Language

This paper describes a Hidden Markov Model (HMM) based (TTS) system and prosody based (TTS) system for producing natural sounding synthetic speech in Tamil language. The (HMM) based system consists of two phases such as training and synthesis. Tamil speech is first parameterized into spectral and excitation features using Glottal Inverse Filtering (GIF). An emotions present in the input text is modeled based on the parametric features. The performance measure has been carried out with recorded speech and the (HMM) based (TTS) system. Subsequently the (TTS) system with prosodic features for generating human voice has been implemented. To produce the output of (TTS) in the same form as if it is actually spoken the prosody feature allows the synthesizer to vary the pitch of the voice. The pitch and duration play an important role to improve the naturalness of (TTS) output. The performance measure has been carried out with recorded speech and the prosody based (TTS) system. Finally the performance of (HMM) based (TTS) has been compared with prosody based (TTS) to measure the effectiveness of the system. Both (TTS) systems are used to analyze the emotions such as Happy, Fear, Neutral and Sad to improve the effectiveness of the system.