# The Domain Mismatch Problem in the Broadcast Speaker Attribution Task  
[PERSON]  
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[PERSON]  
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[PERSON]  
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and [PERSON]  
The study of speaker attribution has been largely studied, as shown in the bibliography. Usually, it has been considered an evolution of diarization. Thus, very often, it follows a similar Bottom-Up strategy: First, the input audio is divided into segments under the assumption of a single speaker per audio fragment. Then, each segment is transformed into a speaker representation and assigned to its corresponding cluster according to its speaker. Within the bibliography, speaker attribution has analyzed multiple sorts of speaker representations: Joint Factor Analysis (JFA) [1], i-vectors [2], PLDA [3], or DNN embeddings, such as x-vectors [4]. Regarding systems, some alternatives rely on Agglomerative Clustering [5; 6; 7] taking into account different metrics (cosine distance, Kullback-Leibler divergence, Cross Likelihood Ratio (CLR), etc ). Other contributions [8; 9] exploit Information Theory concepts, such as Mutual Information, to make decisions. The assignment of clusters by means of a speaker recognition paradigm has also been proposed in [10]. Finally, ref. [11] proposes graph-based semi-supervised learning approximation to speaker attribution. Most of these systems consider well-known techniques and state-of-the-art approximations. However, they usually suffer from a similar limitation: lack of robustness. These systems rely on a threshold under the assumption of similar conditions in both development and evaluation scenarios.  
The motivation for this article is the analysis of speaker attribution in broadcast data. The broadcast domain and its archive services are keen on many types of automatic annotation, including speaker attribution. This interest is due to the increase of produced content experiments within recent years as well as the high complexity of the domain nature. This content, understood as a collection of shows and genres with particular characteristics, provides a challenging domain that requires techniques capable of dealing with such variability. In fact, this complexity motivates the division of our main objective, the analysis of speaker attribution, into three partial goals. These partial goals are:  
* To study the influence of diarization on the performance of speaker attribution systems;
* To analyze the impact of domain mismatch between models and data;
* To propose robust approximations that mitigate the domain mismatch between models and data under analysis.  
The audio used in this study belongs to the latest of the ongoing series of [PERSON] evaluations [12]. These evaluations seek the evolution of speech technologies, such as Automatic Speech Recognition (ASR), diarization and speaker attribution, with special emphasis on the broadcast domain. For this purpose, the whole corpus, gathered along the multiple editions, consists of audio from real broadcast content from radio stations and TV channels. Regarding the 2020 edition, the data are released by Radio Television Espanola (RTVE), the Spanish public Radio and Television Corporation (RTVE collaboration through [[http://catedartrve.unizar.es/](http://catedartrve.unizar.es/)]([http://catedartrve.unizar.es/](http://catedartrve.unizar.es/)), accessed on 9 September 2021).  
This article is organized as follows: A study of the speaker attribution problem is carried out in Section 2. The experimental scenario is described in Section 3. The studied systems are explained in detail in Section 4. Section 5 is dedicated to the results of the experiments carried out in this article. Finally, our conclusions are collected in Section 6.