Speaker recognition

speaker recognition matlab

In the recognition stage, an input utterance is vector-quantized by using the codebook of each reference speaker; the VQ distortion accumulated over the entire input utterance is used for making the recognition determination.

In forensic applications, it is common to first perform a speaker identification process to create a list of "best matches" and then perform a series of verification processes to determine a conclusive match.

Speaker recognition ppt

A normalization method based on a posteriori probability has also been proposed. If the match is good enough, that is, above a threshold, the identity claim is accepted. The fundamental difference between identification and verification is the number of decision alternatives. Another advantage of text-independent recognition is that it can be done sequentially, until a desired significance level is reached, without the annoyance of the speaker having to repeat key words again and again. These systems operate with the users' knowledge and typically require their cooperation. It is well known that samples of the same utterance recorded in one session are much more highly correlated than tokens recorded in separate sessions. In other words, phoneme-classes and speakers are simultaneously recognized in these methods.

Text-Independent Speaker Recognition Methods In text-independent speaker recognition, generally the words or sentences used in recognition trials cannot be predicted.

Scholarpedia, 2 2 This was done for each of the digits making up the input utterance.

speaker recognition tutorial

Therefore, text-independent methods have attracted more attention. Therefore, attempts have been made to find efficient ways of compressing the training data using vector quantization VQ techniques.

speaker recognition tensorflow

High-level Speaker Recognition High-level features such as word idiolect, pronunciation, phone usage, prosody, etc.

Rated 7/10 based on 30 review
Papers With Code : Speaker Recognition