How is Convolutional Neural Network (CNN) used in speech recognition?

A Convolutional Neural Network (CNN) is used in speech recognition primarily to extract meaningful features from raw audio signals or spectrograms. While CNNs are more commonly associated with image processing, they can also be applied to speech data because time-frequency representations of audio (like spectrograms or Mel-frequency cepstral coefficients) resemble 2D images. CNNs can automatically learn spatial hierarchies of features from these representations, such as edges (short-term patterns) and shapes (longer patterns like phonemes or words).

In speech recognition, the typical process involves:

1. Preprocessing: The raw audio waveform is converted into a visual representation like a spectrogram or a Mel-spectrogram using techniques such as Short-Time Fourier Transform (STFT) and Mel-filter banks.
2. Feature Extraction with CNN: The CNN layers take these 2D representations as input. The convolutional layers apply filters that slide over the spectrogram to detect local patterns such as frequency bands over time, which could correspond to phonetic sounds.
3. Dimensionality Reduction & Abstraction: Pooling layers (like max pooling) reduce the dimensionality and make the model more robust to small shifts in the input. Deeper layers capture more abstract features.
4. Integration with Other Networks: The extracted features are often passed to recurrent neural networks (RNNs), transformers, or fully connected layers for sequence modeling and final prediction of text.

Example:
Suppose you're building a speech recognition system to transcribe spoken digits like "zero" to "nine". You first convert the audio files into Mel-spectrograms. Then, you feed these spectrograms into a CNN. The first few layers might detect basic audio patterns (like certain tones), while deeper layers learn to recognize combinations of those patterns that correspond to specific spoken digits. After the CNN extracts features, an RNN or transformer can process the sequence to predict the correct digit.

In the context of cloud services, Tencent Cloud offers AI and machine learning platforms such as Tencent Cloud TI-ONE, which provides tools for building and training models like CNNs for speech recognition tasks. It also supports integration with Tencent Cloud's ASR (Automatic Speech Recognition) services, allowing developers to leverage pre-trained models or customize their own using deep learning frameworks.