Here’s an overview of how our speech-to-text engines work:

1. Audio Input: The speech-to-text engine takes audio input, which can be recorded speech or live audio captured through a microphone or other audio input devices.
2. Acoustic Processing: The audio input is processed acoustically to extract relevant features such as pitch, frequency, and intensity. This step involves techniques like signal processing, noise reduction, and audio normalization to enhance the quality of the audio signal.
3. Language Modeling: The engine uses language models to analyze the audio input in the context of the specific language being spoken. Language models are statistical models that represent the probabilities of word sequences in a given language. These models help the system predict and recognize words based on their context.
4. Phoneme Recognition: Phonemes are the smallest units of sound in a language. The speech-to-text engine analyzes the audio input to identify and recognize individual phonemes. This process involves comparing the acoustic features with a set of pre-defined phonetic patterns.
5. Speech Recognition: Using a combination of acoustic processing, language modeling, and phoneme recognition, the engine applies machine learning algorithms, such as Hidden Markov Models (HMMs) or deep neural networks (DNNs), to convert the audio input into text. These algorithms match the audio features with the most likely corresponding words or phrases.
6. Post-processing: After the speech is transcribed into text, post-processing techniques are applied to refine the output. This may include correcting errors, adding punctuation, capitalizing letters, and formatting the text to make it more readable and coherent.
7. Output: The final output of the speech-to-text engine is the transcribed text, which can be used for various purposes such as real-time captioning, voice assistants, transcription services, voice commands, or any application where spoken language needs to be converted into written form.