Sentence examples for false speakers from inspiring English sources

The System 1, for instance, though detecting less correct speakers, maintains a significantly lower number of false speakers.

Here, the Systems 5 and 4 exhibit the highest number of true detected speakers, but at the same time suffer from even higher counts of false speakers.

The possible reason for the high number of false speakers of System 4 could be the substantial initial over-segmentation (reported in Section 'System 4') in a combination with a too strictly defined merging threshold of the dot-scoring similarity.

Furthermore, a decomposition considering missed-speech detection, false alarms, and false speaker labeling is also depicted in Figure1.

The second metric is the overall speaker diarization error rate (DER), which involves the missed and false alarm speaker times.

Correctly detected (True) and falsely introduced (False) number of speakers by evaluated systems.

Firstly, we review the main evaluation metric (DER) and discuss the distributions of its three components (misses, false alarms, speaker errors).

Scores are given for missed speech (MS), false alarms (FA), speaker errors (SPK), and overall diarization error rate (DER).

A given speaker might use 'I am tired' to express a false proposition, while another speaker uses the same sentence at the same time to express a true proposition.

DER represents the ratio of incorrectly attributed speech time The DER can be broken down into speaker errors (SPKE), which accounts for miss-attributed speaker segments, false alarms (FA), and missed speech errors (MS).

In order to be an observation sentence, he said, a sentence must be contingently true or false, and such that competent speakers of the relevant language can quickly and unanimously decide whether to accept or reject it on the basis what happens when they look, listen, etc. in the appropriate way under the appropriate observation conditions (Feyerabend 1959, 18ff).