Speaker Recognition and Diarization

Identify and verify speakers, perform speaker diarization

Speaker recognition involves identifying who is speaking and verifying the identities of speakers in speech signals. Speaker diarization is the process of segmenting a speech signal based on the identity of the speakers. The following examples demonstrate how to apply a variety of AI techniques to speaker recognition and diarization workflows.

Functions

speakerEmbeddings Extract speaker embeddings from speech (Since R2024b)

Featured Examples

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Speaker Diarization Using Pretrained AI Models

Use the speakerEmbeddings function to extract compact speaker representations and perform speaker diarization.

Since R2024b
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Speaker Identification Using Pitch and MFCC

Use machine learning to identify people based on features extracted from recorded speech.

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Speaker Recognition Using x-vectors

Develop an x-vector system to perform speaker recognition.

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Speaker Diarization Using x-vectors

Speaker diarization is the process of partitioning an audio signal into segments according to speaker identity. It answers the question "who spoke when" without prior knowledge of the speakers and, depending on the application, without prior knowledge of the number of speakers.

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Speaker Verification Using i-vectors

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) model. [11] and [12] proposed a method to Gaussianize the i-vectors and therefore make Gaussian assumptions in the PLDA, referred to as G-PLDA or simplified 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].

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Speaker Verification Using Gaussian Mixture Model

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).

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Speaker Identification Using Custom SincNet Layer and Deep Learning

Perform speech recognition using a custom deep learning layer that implements a mel-scale filter bank.

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