## 7 Human Assisted Cross-Show Diarization
### Cross-Show Question Generation Module
Due to a high cross-show variability, we found that the first option is not optimal, probably because TV and radio shows have high acoustic variability for which a single average \(x\)-vector computed on all of them would not be a good representative of the speaker; for this reason we only focus in this article on two methods: _Averaging_ of \(x\)-vectors per show and _No averaging_, where all possible \(x\)-vectors are kept for comparison. The pseudo-distance matrix is computed by comparing each speaker single \(x\)-vector from the current show to all past-speakers _Averaging_ and _No averaging_ representations. A threshold, set empirically on the development set, is then applied on the pseudo-distances. If a speaker from the current show has no distance below the threshold (see Figure 4) it is categorized as new (never seen in the past). Other speakers are categorized as possibly recurrent and selected for the following closed-set speaker identification phase. In a second step, human-assisted closed-set identification is applied for all speakers categorized as possibly recurrent. For each possibly recurrent speaker, \(x\)-vectors from all _known_-speakers (i.e., _Averaging_ or _No averaging_ representations of those speakers) are sorted by increasing pseudo-distance (Note that the number of those \(x\)-vectors depends on the chosen representation: _averaging_ or _no averaging_). By increasing pseudo-distance, a binary question is asked to the human operator to compare the couples of speakers. This question makes use of one single audio segment selected for each speaker. Similarly to the within-show human-assisted process, the human operator is offered two audio segments (one belongs to the current speaker and one belongs to a known-speaker) to listen to: the longest for each speaker. Based on those two segments, the question asked to the human expert is: \"Is the speaker from this segment (a known-speaker) the same as the one speaking in this current segment?\". If the operator answers \"Yes\", the two speakers are linked and the selected _known_-speaker is not proposed anymore to link with any other current speaker. If the operator answers \"No\", the next \(x\)-vector per order of pseudo-distance is considered. For one current speaker, the process ends either when linked with a _known_-speaker or after a number of questions chosen empirically; in the latest case, the current speaker is added to the _known_ speaker database. Cross-show acoustic variability can pollute \(x\)-vectors and cause errors in their pseudo-distance sorting. In other words, two segments with a higher acoustic mismatch can have lower pseudo-distance, which misleads the close-set speaker identification. We assume  
Figure 4: Detection of new and known speakers for the current show by comparing speakers from the current show with known-speakers from the existing speaker database. All speakers from the current show who do not obtain at least one verification score lower than a fixed threshold are labeled as new speakers; others are then considered for human-assisted closed-set identification. here that the shows are homogeneous enough so that if the current speaker has appeared in past show (so that actually \(S_{a}^{N+1}\) is the same as \(S_{x}\)), then \(S_{x}\) pseudo-distance to \(S_{a}^{N+1}\) must be lower than other speakers in the current show. This assumption motives us to keep only the most similar speaker (the one with lower pseudo-distance) in each past show to include in the checking list. In order to find the best match of speakers cross-show (a speaker from current show and a speaker from past shows), two strategies are proposed: (i) _Nearest speaker per show_: in which one single speaker per show is included in the list (with respect to the pseudo-distance order) as candidate for matching with the current speaker. (ii) _All:_ in which all speakers for all shows are ranked with respect to their pseudo-distance to the current speaker without limiting of number of speaker per show.