5.3 Speaker recognition
5.3.1 Task Description
Speech signal consists of information on various characteristics of a speaker, such as origin,
identity, gender, emotion, etc. This property of speech allows speech-based speaker profiling with a
wide range of applications in forensics, recommendation systems, etc. The research on recognizing
speakers is extensive and aims to solve two major tasks: speaker identification (what is the identity?)
and speaker verification (is the speaker he/she claims to be?). Speaker recognition/verification tasks
require extracting a fixed-length vector, called speaker embedding, from unconstrained utterances.
These embeddings represent the speakers and can be used for identification or verification tasks.
Recent state-of-the-art speaker-embedding-extractor models are based on DNNs and have shown
superior performance on both speaker identification and verification tasks.
AReviewofDeepLearningTechniquesforSpeechProcessing