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.

Demonstrates a machine learning approach to identify people based on features extracted from recorded speech. The features used to train the classifier are the pitch of the voiced segments of the speech and the mel-frequency cepstrum coefficients (MFCC). This is a closed-set speaker identification: the audio of the speaker under test is compared against all the available speaker models (a finite set) and the closest match is returned.

Isolate a speech signal using a deep learning 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).

Denoise speech signals using deep learning networks. The example compares two types of networks applied to the same task: fully connected, and convolutional.

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

Detect regions of speech in a low signal-to-noise environment using deep learning. The example uses the Speech Commands Dataset to train a Bidirectional Long Short-Term Memory (BiLSTM) network to detect voice activity.

Classify the gender of a speaker using deep learning. The example uses a Bidirectional Long Short-Term Memory (BiLSTM) network and Gammatone Cepstral Coefficients (gtcc), pitch, harmonic ratio, and several spectral shape descriptors.

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.

Classify the genre of a musical excerpt using wavelet time scattering and the audio datastore. In wavelet scattering, data is propagated through a series of wavelet transforms, nonlinearities, and averaging to produce low-variance representations of the data. These low-variance representations are then used as inputs to a classifier.

A typical workflow for feature selection applied to the task of spoken digit recognition.

Illustrates a simple speech emotion recognition (SER) system using a BiLSTM network. You begin by downloading the data set and then testing the trained network on individual files. The network was trained on a small German-language database [1].

Create a multi-model late fusion system for acoustic scene recognition. The example trains a convolutional neural network (CNN) using mel spectrograms and an ensemble classifier using wavelet scattering. The example uses the TUT dataset for training and evaluation [1].

Speaker verification, or authentication, is the task of verifying that a given speech segment belongs to a given speaker. In speaker verification systems, there is an unknown set of all other speakers, so the likelihood that an utterance belongs to the verification target is compared to the likelihood that it does not. This contrasts with speaker identification tasks, where the likelihood of each speaker is calculated, and those likelihoods are compared. Both speaker verification and speaker identification can be text dependent or text independent. In this example, you create a text-dependent speaker verification system using a Gaussian mixture model/universal background model (GMM-UBM).

Speaker verification, or authentication, is the task of confirming that the identity of a speaker is who they purport to be. Speaker verification has been an active research area for many years. An early performance breakthrough was to use a Gaussian mixture model and universal background model (GMM-UBM) [1] on acoustic features (usually mfcc). For an example, see Speaker Verification Using Gaussian Mixture Models. One of the main difficulties of GMM-UBM systems involves intersession variability. Joint factor analysis (JFA) was proposed to compensate for this variability by separately modeling inter-speaker variability and channel or session variability [2] [3]. However, [4] discovered that channel factors in the JFA also contained information about the speakers, and proposed combining the channel and speaker spaces into a total variability space. Intersession variability was then compensated for by using backend procedures, such as linear discriminant analysis (LDA) and within-class covariance normalization (WCCN), followed by a scoring, such as the cosine similarity score. [5] proposed replacing the cosine similarity scoring with a probabilistic LDA (PLDA). While i-vectors were originally proposed for speaker verification, they have been applied to many problems, like language recognition, speaker diarization, emotion recognition, age estimation, and anti-spoofing [10]. Recently, deep learning techniques have been proposed to replace i-vectors with d-vectors or x-vectors [8] [6].

Perform pitch tracking using multiple pitch estimations, octave and median smoothing, and a hidden Markov model (HMM).

Time-frequency masking is the process of applying weights to the bins of a time-frequency representation to enhance, diminish, or isolate portions of audio.

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