Speech Transcription and Synthesis

Use third-party APIs for text-to-speech and speech-to-text

Audio Toolbox™ provides examples for small-vocabulary recognition and sound synthesis. To perform general text-to-speech and speech-to-text, Audio Toolbox provides interfaces to popular third-party APIs. Supported APIs include Google^® Speech, IBM^® Watson Speech, and Microsoft^® Azure Speech. To use this functionality, you must download the Audio Toolbox extended functionality for text2speech and speech2text from File Exchange.

Once you install the speech-to-text functionality, you can interact with it graphically in the Audio Labeler app to quickly label regions of speech.

Apps

Audio Labeler

Define and visualize ground-truth labels

Functions

Speech-to-Text Transcription

Text-to-Speech Conversion

Topics

Label Audio Using Audio Labeler

Interactively define and visualize ground-truth labels for audio datasets.

Featured Examples

Speech Command Recognition Using Deep Learning

Train a deep learning model that detects the presence of speech commands in audio. The example uses the Speech Commands Dataset [1] to train a convolutional neural network to recognize a given set of commands.

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Keyword Spotting in Noise Using MFCC and LSTM Networks

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).

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Spoken Digit Recognition with Wavelet Scattering and Deep Learning

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.

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