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RSS Curriculum Vitae TR

Speaker Embedding

Turkish equivalent: Konuşmacı gömme vektörüDomain: Speech Processing

A fixed-dimensional dense vector intended to encode speaker-related characteristics from a speech segment.

Representation

A speaker embedding maps a variable-length speech segment into a fixed-dimensional vector. The objective is for segments from the same speaker to occupy nearby regions of the learned space while different speakers are separated.

Comparison

Verification systems can score two embeddings with cosine similarity, PLDA or another backend. In diarization, embeddings are commonly used as the representation supplied to clustering.

The model, segmentation strategy and decision threshold have to be validated together.

Data Quality

Segment duration, overlapping speech, channel, codec, noise and domain mismatch can all affect the vector. A short or incorrectly segmented input can produce an unreliable representation even with a strong model.

An embedding is not a content-free identity number. Biometric use also requires explicit storage, access-control and privacy decisions.

Related article: Audio Feature Vectors and Matching

Updated:

Related concepts

Speech Processing

Related technical publications

Publications whose title or summary directly references this concept.

Speaker Recognition and Diarization

Speaker verification and identification, text-dependent and text-independent recognition, GMM-UBM, i-vectors, speaker embeddings, threshold calibration, and diarization.

Audio Feature Vectors and Matching

Audio feature extraction through pre-emphasis, framing, windowing, FFT/STFT, time and spectral features, MFCCs, pitch and formants, and learned audio or speaker embeddings.