Corvolutional neural networks are more suitable for dealing with speech recognition problems

Question

Options

• ATrue
• BFalse

• A
  29% (19)
• B
  71% (47)

Explanation

Recurrent Neural Networks (RNNs) and their variants, such as LSTMs, are far more suitable for speech recognition because speech is an inherently sequential, temporal signal -- meaning the order and timing of sounds carry critical meaning. CNNs are optimized for detecting local spatial patterns in data with a grid-like structure, such as images, making them the dominant choice for computer vision rather than speech. While CNNs can play a supporting role (for example, extracting features from a spectrogram), they are not the primary or most suitable architecture for speech recognition as a whole.

Option A is wrong because it incorrectly assigns CNNs a role they were not designed to fill; the temporal dependencies in speech require architectures that maintain state across time steps, which CNNs do not do.

Memory tip: Match the data structure to the network type. CNNs handle space (images have height and width), while RNNs handle time (speech unfolds across a timeline). If the data flows through time, reach for a recurrent or attention-based model, not a convolutional one.

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