How Speech To Text Works: From Audio Waveforms to Log-Mel Spectrograms
Eric King
Author
Speech To Text technology is now widely used in meeting transcription, video subtitles, voice input, and intelligent assistants. But how does a computer actually understand human speech without having ears?
To answer this question, we need to start with the most familiar audio representationβthe audio waveformβand move step by step toward the core feature used in modern ASR systems: the Log-Mel Spectrogram.
Audio Waveform: The Most Familiar Sound Representation
In audio recording or editing tools, sound is typically displayed as an audio waveform.
An audio waveform shows:
- Time on the horizontal axis
- Amplitude (loudness) on the vertical axis
Waveforms help users visually identify:
- When speech occurs
- Silent or paused segments
- Changes in volume
However, for Speech To Text systems, waveforms only describe how loud a sound isβnot what the sound actually is.
Why Waveforms Are Not Enough for Speech To Text
The true linguistic information in speech lies in its frequency content, not just its amplitude.
Different phonemes, voices, and speaking styles are defined by how frequencies are combined and evolve over time. In a waveform, these details are hidden inside complex oscillations, making direct interpretation difficult for machines.
That's why Speech To Text systems convert audio from the time domain into the frequency domain.
From Waveform to Spectrogram: Visualizing Frequency
To analyze speech more effectively, ASR systems generate a spectrogram, which shows:
- Time on the x-axis
- Frequency on the y-axis
- Color intensity representing energy
A spectrogram reveals how frequency components change over time, making it easier to identify speech patterns. Still, raw spectrograms do not fully match how humans perceive sound.
Log-Mel Spectrogram: The Core Feature of Speech To Text
This is where the Log-Mel Spectrogram comes in.
It improves upon a standard spectrogram by:
- Mapping frequencies to the Mel scale, which aligns with human auditory perception
- Applying logarithmic compression to reduce sensitivity to volume differences
The result is a two-dimensional "sound image" that clearly captures:
- Phonetic structures
- Voice characteristics
- Temporal speech patterns
Modern Speech To Text models, including Whisper, use Log-Mel Spectrograms as their primary input.
Why Log-Mel Spectrograms Are Essential for Speech To Text
Log-Mel Spectrograms offer several advantages:
- Closer alignment with human hearing
- Clearer separation of phonemes
- Greater robustness to noise and volume changes
- Better suitability for deep learning models
They represent the crucial step from simply detecting sound to truly understanding speech.
Conclusion
Speech To Text is not just about processing audioβit's about understanding speech structure. Audio waveforms allow us to see sound, but Log-Mel Spectrograms allow machines to interpret it.
The transformation from waveform to spectrogram to Log-Mel Spectrogram is the foundation behind today's accurate and reliable Speech To Text systems.
