Speech-to-Text Models: How They Work

 🎤 Speech-to-Text Models: How They Work

Speech-to-Text (STT) — also called Automatic Speech Recognition (ASR) — is the technology that converts spoken language into written text. These models are used in voice assistants, transcription tools, customer service, and more.


🧠 Step-by-Step Breakdown

1. Audio Input

The user speaks into a microphone.


The audio is recorded as a waveform — a digital signal that represents the sound.


2. Preprocessing

The waveform is cleaned: removing background noise, normalizing volume, etc.


The audio is broken into small time segments (called frames).


Then it’s converted into features like:


MFCC (Mel Frequency Cepstral Coefficients)


Spectrograms


These features represent how sound frequencies change over time.


3. Acoustic Model

This model maps sound features to phonemes — the smallest units of sound in a language.


Deep learning models like Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) are often used here.


4. Language Model

Once phonemes or word parts are recognized, a language model helps make sense of them.


It predicts the most likely sequence of words, correcting errors and improving accuracy.


Modern systems use transformers (like BERT or GPT) for this step.


5. Decoder

The decoder combines information from the acoustic model and the language model.


It outputs the final written text that represents what was spoken.


⚙️ Types of STT Models

Traditional Models: Use separate components (acoustic, language, pronunciation models).


End-to-End Models: Use a single deep learning model (like RNN-Transducer, CTC, or Transformer-based models) to directly map audio to text.


🧪 Popular Architectures

DeepSpeech (by Mozilla) – Based on RNN + CTC loss.


Wav2Vec 2.0 (by Meta) – Uses self-supervised learning on raw audio.


Whisper (by OpenAI) – A transformer-based model trained on many languages and tasks.


✅ Real-World Applications

Voice assistants (Siri, Alexa, Google Assistant)


Live captions


Call center analytics


Dictation tools


Meeting transcription (like Zoom or Otter.ai)

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