stimmsynthese

Stimmsynthese, also known as speech synthesis or text-to-speech (TTS), is a technology that artificially generates human speech. It involves converting written text into spoken words. The process typically begins with text normalization, where abbreviations, numbers, and punctuation are converted into their spoken equivalents. This is followed by phonetization, where the normalized text is broken down into phonetic representations. Finally, prosody generation and waveform synthesis create the actual audio output.

There are two main approaches to stimmsynthese: concatenative and parametric. Concatenative synthesis uses pre-recorded speech units, such as phonemes or diphones, which are then pieced together to form the desired speech. This method can produce very natural-sounding speech, but requires a large database of recordings. Parametric synthesis, on the other hand, models the speech signal using parameters, such as fundamental frequency and spectral envelopes, which are then used to generate the audio. This approach is more flexible and requires less storage space but can sometimes result in less natural-sounding speech.

Recent advancements in deep learning, particularly neural networks, have significantly improved the quality and naturalness of stimmsynthese. Neural TTS systems can learn complex relationships between text and speech, leading to more human-like intonation, rhythm, and voice quality. These systems are increasingly being used in applications like virtual assistants, accessibility tools, and audiobooks. The goal of stimmsynthese is to enable machines to communicate audibly with humans in a clear and engaging manner.