A spectrogram is a visual representation of how the frequencies in a sound signal change over time. It is created by breaking an audio signal into short segments and applying a mathematical tool called the Short-Time Fourier Transform (STFT) to each segment. This process converts the raw waveform—a time-domain signal—into a time-frequency representation. The x-axis of a spectrogram represents time, the y-axis represents frequency, and the color or intensity at each point indicates the amplitude (or energy) of a specific frequency at a given moment. For example, a high-energy burst at 1,000 Hz lasting 0.5 seconds would appear as a bright horizontal band at that frequency on the spectrogram. This format simplifies analyzing complex sounds like speech, where multiple frequencies (such as pitch and formants) overlap.
In speech recognition, spectrograms serve as a critical preprocessing step. Raw audio waveforms are high-dimensional and noisy, making them challenging for machine learning models to process directly. By converting speech into a spectrogram, the model can focus on meaningful patterns in frequency and time. For instance, phonemes—the distinct sound units in language—have unique frequency profiles. Vowels like /a/ or /i/ are characterized by strong low-frequency formants, while fricatives like /s/ or /sh/ exhibit high-frequency energy. Spectrograms make these differences visually apparent, enabling models to learn correlations between frequency patterns and linguistic units. Modern speech recognition systems, such as those using convolutional neural networks (CNNs) or transformers, often take spectrograms (or derived features like Mel-Frequency Cepstral Coefficients) as input to identify phonemes, words, or phrases.
To create a spectrogram for speech recognition, developers typically apply specific preprocessing steps. First, the audio is divided into overlapping frames (e.g., 25 ms windows with a 10 ms overlap) to capture short-term frequency changes. Each frame is processed using the STFT, and the resulting frequency bins are often mapped to a Mel scale—a nonlinear scale that approximates human hearing sensitivity—to create Mel spectrograms. For example, a Mel spectrogram might use 128 frequency bands instead of the raw STFT’s linear bins, emphasizing perceptually relevant features. These spectrograms are then normalized and fed into neural networks. In practice, a model trained on spectrograms of the word “cat” would learn to detect the high-frequency burst of the /k/ sound, the steady-state formants of the vowel /æ/, and the abrupt stop of the /t/. This approach reduces computational complexity compared to raw audio and improves accuracy by highlighting phonetically significant features.