# Cascade or Direct Speech Translation? A Case Study +
## 1 Introduction  
Speech translation (ST) systems have been traditionally designed under a cascade approach, where independent automatic speech recognition (ASR) and machine translation (MT) components are chained, feeding the ASR output into the MT component, oftentimes with task-specific bridging to optimise component communication [1, 2, 3]. Although this approach has been the dominant paradigm in the field, cascade speech translation is prone to error propagation, requires the assembly of separate components, and cumulates the latency of its two main components. With the advent of deep learning approaches, significant results have been achieved with end-to-end neural models, notably in the fields of machine translation [4, 5] and speech recognition [6, 7], building on the ability of neural models to jointly learn different aspects of a task. Following the success of these approaches, end-to-end modelling has also been proposed for the speech translation task [8, 9, 10]. In what follows, we use the terms _directand _end-to-end_ interchangeably to denote the process of modelling speech translation with a neural network trained on speech input in the source language and text output in the target language. While preliminary results with end-to-end systems had shown promise, the initial cascade systems still obtained better results overall on standard evaluation datasets [11]. One of the main factors for this state of affairs has been the paucity of parallel speech-text corpora, in contrast with the comparatively larger amounts of data available to train separate ASR and MT models, for some language pairs at least. Recent efforts have been made to build parallel corpora suitable for the task, notably the multilingual MuST-C [12] and Europarl-ST [13] corpora. As most available data are built around English, which limits experimental variety, [14] made available the mintzai-ST corpus for Basque-Spanish. This corpus supports further experimentation on languages with a number of marked linguistic properties, such as Basque, notably rich morphology and relatively free word order, which can represent a challenge for natural language processing tasks in general and automated translation in particular [15,16]. With newly available ST corpora supporting research and development in the field, recent variants of direct ST models have closed the quality gap with cascade approaches [17, 18, 19, 20, 21, 22], in terms of automated metrics or manual evaluations on standard datasets [23,24]. Nevertheless, additional work is still needed to identify the strengths and weaknesses of end-to-end approaches to the task, and comparative results may fluctuate on standard datasets [25]. In this paper, we extend the work of [14] on Basque-Spanish ST and address in more details the comparative merits of cascade and direct approaches on a relatively difficult language pair and dataset. We first explored the characteristics of the mintzai-ST Basque-Spanish corpus, to provide a complete description of the data and present results for the baseline cascade and direct models trained on this corpus. We then describe additional end-to-end variants, which bridge the gap between the two approaches in terms of automated metrics. Finally, we describe the results of manual evaluations comparing the cascade and direct models.