Neural networks power speech recognition by converting audio signals into text through a series of processing stages. Initially, the audio waveform is transformed into spectrograms or Mel-frequency cepstral coefficients (MFCCs), which serve as inputs for the network. Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs) are commonly used to extract temporal and spatial features from these inputs.
RNNs, particularly Long Short-Term Memory (LSTM) networks, are adept at handling sequential data like speech. They capture dependencies and context across time steps, enabling the model to understand the relationship between phonemes, words, and sentences. Attention mechanisms further enhance performance by helping the model focus on the most relevant parts of the input.
End-to-end architectures like Transformer models (e.g., Whisper by OpenAI) have gained popularity for speech recognition. These models directly map audio features to text without requiring intermediate phoneme representations, improving both accuracy and efficiency. Neural networks have significantly advanced speech recognition, making it integral to applications like virtual assistants, transcription services, and accessibility tools.