Speech enhancement techniques in speech recognition aim to improve the quality and intelligibility of speech signals, thereby enhancing recognition accuracy. The main types include:
Spectral Subtraction: This method subtracts estimated noise spectra from the noisy speech spectrum. It assumes the noise is additive and stationary.
Example: Reducing background hum in a quiet office environment.
Wiener Filtering: A statistical approach that minimizes the mean square error between the estimated clean speech and the actual clean speech. It uses the power spectral density of noise and speech.
Example: Enhancing speech in a low-noise call center recording.
Subtractive Methods (e.g., Log-MMSE): These methods improve speech by log-spectral amplitude estimation, often outperforming basic spectral subtraction.
Example: Boosting clarity in noisy restaurant recordings.
Deep Learning-Based Methods (e.g., DNN, RNN, Transformer): Neural networks learn complex mappings between noisy and clean speech. Techniques like Denoising Autoencoders (DAE) or Recurrent Neural Networks (RNN) are commonly used.
Example: Using a Deep Neural Network (DNN) to remove traffic noise from a voice assistant recording.
Beamforming: A microphone array technique that spatially filters sound to focus on the target speaker while suppressing noise from other directions.
Example: Enhancing speech in a conference room with multiple microphones.
Spectral Masking: A technique where a mask (binary or ratio) is applied to the spectrogram to separate speech from noise.
Example: Applying an Ideal Binary Mask (IBM) to isolate speech in a noisy call.
In cloud-based speech recognition, services like Tencent Cloud ASR (Automatic Speech Recognition) often integrate these techniques to improve accuracy. For advanced noise reduction, Tencent Cloud Real-Time Audio Enhancement or Speech Enhancement APIs can be used to preprocess audio before recognition.