A speech recognition system converts spoken language into text by combining several technical components. At a high level, these systems process audio input, extract meaningful features, match them to linguistic patterns, and generate text output. The core components include audio preprocessing, feature extraction, acoustic modeling, language modeling, and decoding algorithms. Each part plays a specific role in transforming raw sound into accurate transcriptions.
The first stage involves audio preprocessing and feature extraction. Raw audio signals are sampled and digitized, then cleaned to remove background noise or irrelevant frequencies. For example, a system might apply a Fourier transform to convert time-domain audio into frequency-domain spectrograms. Features like Mel-Frequency Cepstral Coefficients (MFCCs) are commonly extracted to capture phoneme-level characteristics, such as pitch and tone shifts. These features reduce the complexity of the audio data while preserving details critical for recognition. Developers often use tools like librosa or Python’s signal processing libraries to implement these steps. Preprocessing also involves segmenting audio into smaller frames (e.g., 25ms windows) to analyze short-term patterns.
Next, acoustic and language modeling map features to linguistic units. Acoustic models, traditionally built using Hidden Markov Models (HMMs) or modern neural networks like CNNs or RNNs, associate audio features with phonemes or subword units. For instance, a deep learning model might train on thousands of hours of labeled speech to learn how the sound “th” differs from “sh.” Language models, often based on n-grams or transformer architectures, predict the likelihood of word sequences. They help resolve ambiguities—for example, distinguishing between “their” and “there” based on context. These models are trained on large text corpora to learn grammar, syntax, and common phrases.
Finally, decoding and post-processing combine acoustic and language model outputs to produce the final text. A decoder, such as a weighted finite-state transducer (WFST) or beam search algorithm, efficiently searches for the most probable word sequence given the audio input. For example, a beam search might retain the top five candidate phrases at each step to balance accuracy and computational cost. Post-processing steps include capitalization, punctuation insertion, and correcting homophones (e.g., “write” vs. “right”) using context. Real-world systems often integrate user-specific customization, like adapting to accents or specialized vocabularies (e.g., medical terms), to improve accuracy for specific use cases.