X-vectors are a specialized type of deep learning feature representation used primarily in speaker recognition tasks. They are generated from a deep neural network trained to extract speaker-specific characteristics from audio data. The X-vector model typically consists of a time-delay neural network (TDNN) that processes variable-length audio segments and outputs a fixed-dimensional embedding for each speaker. This representation captures essential features such as vocal quality, pitch, and speaking style, allowing for effective discrimination between different speakers. X-vectors have gained popularity in automatic speech recognition (ASR) systems and are critical in enhancing speaker verification and identification performance.