Speech Recognition

Use a pretrained deep learning model to perform speech command recognition on streaming audio.

Train a deep learning model that detects the presence of speech commands in audio.

Classify spoken digits using both machine and deep learning techniques. In the example, you perform classification using wavelet time scattering with a support vector machine (SVM) and with a long short-term memory (LSTM) network. You also apply Bayesian optimization to determine suitable hyperparameters to improve the accuracy of the LSTM network. In addition, the example illustrates an approach using a deep convolutional neural network (CNN) and mel-frequency spectrograms.

Perform batch and streaming voice activity detection (VAD) in a low SNR environment using a pretrained deep learning model.

Train a BiLSTM network to perform voice activity detection (VAD) in a low SNR environment.

Implement a simple speech emotion recognition system using a BiLSTM network.

Identify a keyword in noisy speech using a deep learning network. In particular, the example uses a Bidirectional Long Short-Term Memory (BiLSTM) network and mel frequency cepstral coefficients (MFCC).

Detect the presence of speech commands in audio using a Simulink^® model.

Use a pretrained deep learning model in Simulink to identify a keyword in speech.