However, in most cases, the quality of the synthesized speech is not ideal. No need to store audio sample in a database The parameters are used to synthesize the final speech waveforms.Īdvantages of statistical parametric synthesis: The one that has been proven to provide the best results historically is the Hidden Markov Model (HMM).ĭuring synthesis, HMMs generate a set of parameters from our target text sequence. We then try to estimate those parameters using a statistical model. During training, we extract a set of parameters that characterize the audio sample such as the frequency spectrum (vocal tract), fundamental frequency (voice source), and duration (prosody) of speech. In statistical parametric synthesis, we generally have two parts. The difference is that we use a function and a set of parameters to modify the voice. Parametric synthesis utilizes recorded human voices as well. At run time, the desired sequence is created by determining the best chain of candidate units from the database (unit selection). We acquire the segments with the help of a speech recognition system and we then label them based on their acoustic properties (e.g. They are usually stored in the form of waveforms or spectrograms.

The segments can be full sentences, words, syllables, diphones, or even individual phones. Concatenation synthesisĬoncatenation synthesis, as the name suggests, is based on the concatenation of pre-recorded speech segments. Over the years there have been many different approaches, with the most prominent being concatenation synthesis and parametric synthesis. A Text To Speech (TTS) system aims to convert natural language into speech. In most applications, text is chosen as the preliminary form because of the rapid advance of natural language systems. Speech synthesis is the task of generating speech from some other modality like text, lip movements, etc.

Wavenet text to speech free