Kõnesünteesitehnikad

Kõnesünteesitehnikad refers to the methods and algorithms used to artificially generate human speech. These techniques aim to produce intelligible and natural-sounding spoken language from written text or other input. The primary goal is to create speech that is indistinguishable from human utterance.

One of the earliest and simplest approaches is concatenative synthesis. This method involves storing pre-recorded units of speech, such as phonemes, diphones, or even entire words and phrases. When new speech needs to be generated, the system selects the appropriate pre-recorded units and concatenates them together. While capable of producing high-quality speech for specific domains, it can suffer from unnatural prosody and transitions between units if not carefully managed.

Another significant technique is formant synthesis. This approach models the vocal tract and its resonances, generating speech by manipulating acoustic parameters like fundamental frequency, formant frequencies, and amplitudes. Formant synthesizers are more flexible and require less storage space than concatenative systems, but often produce robotic or artificial-sounding speech.

Parametric synthesis, often based on hidden Markov models (HMMs) or more recently, deep neural networks (DNNs), represents a more modern approach. These methods learn statistical models of speech from large datasets and generate speech by producing acoustic features (like mel-cepstral coefficients) that are then converted into sound waves by a vocoder. Deep learning-based parametric synthesis has achieved remarkable improvements in naturalness and intelligibility, blurring the lines between artificial and human speech. WaveNet and similar neural vocoders are prime examples of this advanced parametric approach.