# Dialogue Enhanced Extended Reality:
## 2 Background
### Spoken Dialogue Systems  
Spoken Dialogue Systems (SDSs) are voice-enabled Human-Machine Interfaces for natural communication with a computer, robot, and other devices [24; 25]. SDSs rely on processing and exploiting domain knowledge to guide users and solve their needs [26; 27; 28]. One of their main advantages is that they exploit spoken language, rendering a convenient and frictionless system, which communicates in a natural way.  
Additionally, these systems facilitate hands-free access to information, thus enabling online information consumption for processes that require manual work. Also, SDSs can be enhanced with error handling techniques that detect interaction breakdowns during the process due to misunderstanding, channel noise, etc. [29; 30], improving the communication between the user and the system. As SDSs provide a suitable response for the knowledge management and expertisedistribution challenge, it comes as no surprise that they have been used in multiple sectors. To name a few examples, these include: LARRI, a dialogue-based system for support in maintenance and repair activities for aircraft mechanics [28]; the International Space Station procedural assistant [31]; bus scheduling systems [32; 33]; tourism booking systems [34]; resident helping with robotic assistants in nursing homes [35]; elderly assistance and coaching systems [36]; educational dialogue systems for tutoring [37; 38; 39]; and retail assistance [40]. Despite their popularity, past limitations in speech recognition engines and in microphones' noise-cancelling mechanisms prevented the application of SDSs in industrial environments.  
To carry out a flexible and natural communication with the operator, the proposed framework emulates the modular and user-centred SDS architecture. Traditional SDSs are a pipeline of specialised technological modules (see Figure 1). The first module encapsulates a Speech To Text (STT) service to transform audio signals into textual transcriptions. Audio transcriptions are processed by a Spoken Language Understanding (SLU) module and encoded as semantic actions (i.e., the communication intent of the operator, which can be understood as a simplification of what the user meant to say), so the linguistic variability of the input is reduced (e.g., \"Tell me what to do now\", \"What's the next step?\", and \"Now what?\" can all be represented as the same semantic codification: [_intent=ask_, _object=next-step_]).  
Semantic actions are passed down to the Dialogue Manager (DM) module, which is in charge of defining the best strategy to give an appropriate answer to the operator. The interaction strategy relies on real-time flow states and existing domain knowledge. In the last process stage, the interaction feedback computed by the DM module is converted into a human-interpretable interactive action by the Response Generator (RG) and synthesised into audio speech by the Text to Speech (TTS) module.  
The modules that constitute an SDS can be implemented in many different ways and they can integrate a vast range of technologies [41; 42]. The in-detail description of these modules exceeds the scope of this paper. Withal, it is noteworthy that module stack strategies facilitate the deployment of SDSs both in scenarios where enough labelled data are available, where Machine Learning can be successfully applied, and in data scarce scenarios, where mechanisms such as encoding expert rules, designing dialogue flows, and others can be implemented.