Gaussian Mixture Models Model for Speaker Identification/Verification

The Gaussian Mixture Models (GMM) model is a statistical model used for representing the probability distribution of audio features in speaker identification and verification tasks. It is based on the assumption that the probability distribution of each speaker's audio features can be represented as a mixture of multiple Gaussian distributions.

GMMs are trained using labeled audio data, where each label represents a specific speaker. The model learns the parameters of the Gaussian distributions and the mixture weights to represent the variation in the audio features of different speakers.

During speaker identification or verification, the GMM model outputs the probabilities of observing the given audio features for each speaker. The speaker with the highest probability is typically considered as the identified speaker.

1. Speaker Identification: GMMs can be used to identify the speaker from a given speech sample by comparing the likelihoods of the audio features with the trained models for different speakers.
2. Speaker Verification: GMMs can be employed to verify if a given voice sample matches the identity claimed by the speaker. The model calculates the likelihood ratio between the target speaker and impostor models to determine authentication.
3. Forensic Analysis: GMMs can aid in forensic investigations by identifying and comparing speakers across multiple recorded audio sources, such as phone calls or surveillance recordings.

1. Introduction to Gaussian Mixture Models - an overview of GMMs and their applications.
2. Speaker Identification using GMMs - a tutorial on speaker identification using GMMs with Python code examples.
3. Speaker Verification with GMMs - a research paper on speaker verification using GMMs, including implementation details.

1. Debanshu Kundu - Extensive contributions to speaker verification systems based on GMM techniques.
2. Ming Zeng - Researcher focusing on robust speaker identification using GMMs and deep neural networks.
3. Omid Abbasarapour - Active contributor to open-source projects related to speaker identification, including GMM-based approaches.
4. Xinshi Chen - Experienced in building GMM-based speaker recognition systems, with a focus on deep learning.
5. Diego Castán - Researching and implementing GMM-based solutions for speaker identification and verification.

Note: The expertise of these individuals may extend beyond GMMs for speaker identification/verification.