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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 LabelerDefine 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

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

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

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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Train Generative Adversarial Network (GAN) for Sound Synthesis

Train Generative Adversarial Network (GAN) for Sound Synthesis

Train and use a generative adversarial network (GAN) to generate sounds.

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LPC Analysis and Synthesis of Speech

LPC Analysis and Synthesis of Speech

Use the Levinson-Durbin and Time-Varying Lattice Filter blocks for low-bandwidth transmission of speech using linear predictive coding.

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Audio Toolbox Documentation

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