# Context-Based Model for Browsing the Web Through Voice
## 2 Related Work
### Voice-Based Web Browsing  
According to [PERSON] and [PERSON] [31], speech is the most natural form of communication for humans, and, at the same time, it is one of the most difficult modalities for machines to understand. But, thanks to advances in voice interfaces, this mode of interaction has become popular, and, under this approach, multiple tools have been developed, such as those described in Section 2.1. Below, we analyze works related to web browsing which use voice-based interfaces and deliver content in audible format with the aim of improving user interaction or facilitating accessibility for people at risk of technological exclusion.  
[PERSON] et al. [32] present an extension for the Google Chrome web browser as a complement to the JAWS screen reader. Its objective is to facilitate the interaction of people suffering from partial or total vision loss with data records in modern web applications. This proposal identifies and manages interest segments on web pages to display leaked information in an alternate web interface and deliver information by voice using JAWS. However, the prototype only works in a predetermined test environment.  
SaIL [14] is an automatic system that detects important information from websites using a machine learning algorithm and generating reference points in the ARIA standard that determine the location and sequence of the identified information. It is trained with information obtained from the search context and the most visited information on a web page. SaIL is compatible with the Google Chrome browser and facilitates navigation through JAWS screen readers.  
iTOC [13] is a browser extension that automatically identifies and extracts hyperlinks from tables of contents in web documents to allow the JAWS screen reader quick, on-demand access to each item within the table. Its goal is to improve JAWS users' interaction with long web documents. It is compatible with the Google Chrome web browser.  
[PERSON] et al. [11] propose a semantic abstraction-based system, whose input is voice commands in natural language with the objective of quickly locating and accessing specific parts of websites related to purchases, reservations, and registrations. The study is based on the general characteristics of 100 popular websites. The system segments and filters useful web page elements based on the interpretation of predefined commands. To achieve this, it uses a library of search terms, so there is no semantic analysis of user instructions, and it is oriented to actions in which it is necessary to interact with buttons, cursor, and registers.  
[PERSON] et al. [33] develop Ho2 iev, a mechanism for high-precision information extraction using heavyweight ontologies[PERSON] It also offers an integrated voice command recognition system for visually impaired Internet users. However, Ho2 iev does not perform natural language processing or allow users to interact with the content of web pages. In addition, communication with the web server is done via short message service (SMS)[PERSON]  
Hearsay [12] is a non-visual and multimodal web browser whose goal is to solve some web accessibility problems. It is a desktop application that can also be used remotely via landline phones and is compatible with IBM's social accessibility network. It performs content analysis and context management of web pages. Recognition of voice commands is based on a statistical model. Hearsay supports voice, text, and touch screen gestures as input modes and audio, visual, and Braille output modes. In addition, it converts web page content into interactive dialog for the user based on the VoiceXML standard. However, Hearsay does not semantically analyze user requests or process natural language, but rather handles specific, predetermined voice commands.