An advanced software program created to process, evaluate, and analysis human speech is known as a voice engine.
With the widespread use of voice over internet protocol technology in software DSP systems around 2000, the name gained popularity.
Unlike earlier generations of systems that needed specialized, math-optimized digital signal processor chips,
speech engines execute the voice processing for an IP Phone system on a normal CPU.
Fundamentally, it uses algorithms that translate text into words that are spoken (text-to-speech synthesis)
and spoken words into text (speech recognition).
To properly reproduce spoken language, speech recognition technology breaks down the audio input into smaller pieces,
such phonemes or words, and compares them to a large database of linguistic patterns.
In order to learn from data and gradually increase accuracy, this method could make use of machine learning techniques.
In order to produce speech, this approach often uses neural networks to generate speech directly from text or generates speech waveforms
based on linguistic rules and pre-recorded human voice segments.
Virtual assistants, dictation software, navigation systems, and accessibility tools are just a few of the gadgets and apps that depend heavily on voice engines.
Voice engines are become more complex as technology develops, providing more naturalness, accuracy, and language and accent flexibility.
In order to bring consumers more context-aware and tailored experiences, they are also being combined with other AI technologies more and more.
