# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task
## 2 The Speaker Attribution Problem  
The speaker attribution problem is a complex task focused on inferring detailed speaker information about any input audio. As illustrated in Figure 1, speaker attribution should estimate whether a set of enrolled people contribute to a given audio and, if so, when they talk. These decisions are made according to small portions of audio from the speakers of interest. Whenever all speakers in the audio are reassured to be enrolled in the system, we are dealing with a closed-set scenario; otherwise, it is an open-set scenario. Some contributions in the field of speaker attribution are [13], which focuses on the identity assignment and includes purity quality metrics, [14], which combines the diarization task with a speaker-based identity assignment, and [15], which speeds up the diarization and the identity assignment process by means of low-resource techniques. State-of-the-art speaker attribution has been built by collecting techniques to identify the audio from a single speaker in a recording and strategies to assign this audio to the corresponding enrolled speaker. This description also fits other tasks, such as speaker linking and longitudinal diarization, which require similar techniques and only differ in the description of enrollment audios. A general solution for all these tasks follows a diagram block, as shown in Figure 2.  
This diagram represents a Bottom-Up approach for speaker attribution. First, the given audio is divided into segments in which a single speaker is assumed. These segments are then transformed into representations that highlight the speaker discriminative properties. Taking into account these representations, the segments are clustered, grouping those with a common speaker. Finally, each one of these clusters are assigned to an identity, either an enrolled speaker or the generic unknown one. For this purpose, a side pipeline processes those audios belonging to the enrollment identities in an offline fashion. The described diagram has an alternative interpretation: A Bottom-Up diarization system isolates the speakers in the audio under analysis, assigning them to the corresponding identity afterwards. Regardless of the interpretation, most of these blocks are based on popular techniques from both speaker recognition and diarization. The contribution of speaker recognition consists of tools to accurately represent the speakers by means of the embedding-backend paradigm. Thus, the speech of a speaker in a given piece of audio is transformed into a compact representation that highlights its discriminative properties. Some generative statistical alternatives are Joint Factor Analysis [1] or the well-known i-vectors [2]. With the advent of Deep Learning (DL), discriminative embeddings based on neural networks have also been proposed, such as x-vectors [4] and d-vectors [16]. On top, a backend is in charge of scoring how likely the speaker in the test audio is an enrolled speaker. As backend, the Probabilistic Linear  
Figure 1: Concept diagram of speaker attribution. For the given audio, we assign the portion of speech generated by each enrolled speaker. Additionally, we must also detect the audio belonging to non-enrolled speakers (red arrow). Figure 2: General block diagram for a speaker attribution system. Discriminant Analysis (PLDA) [3] has been traditionally considered, although certain modifications, such as discriminative PLDA [17], the heavy-tailed PLDA [18; 19] or the Neural PLDA [20], are now popular too. Unfortunately, all these techniques work under the assumption of a single speaker within the given recording; otherwise, the performance is severely degraded. Due to the fact that speaker recognition techniques cannot deal with multiple speakers, diarization is in charge of isolating the audio from each speaker. In order to do so, diarization contributes with two stages: Segmentation identifies contiguous segments with a single speaker, and Clustering groups those segments that share a common speaker.