Review of Speech-to-Text Recognition Technology for Enhancing Learning. (2014)

Introduction Recent evidence suggests that some challenges and limitations exist in physical and online synchronous learning environments that still require attention to solve them (Camiciottoli, 2005; Miller, 2007; Chen, Ko, Kinshuk, & Lin, 2005; Huang & Chiu, 2014; Neilsen, 2009; Nisbet & Spooner, 1999; Shadiev, Hwang, & Huang, in press; Wang, Chen, & Levy, 2008). For example, on an academic event, information is usually addressed through audio channels so that students with learning or physical disabilities, foreign students, and other at risk populations are challenged to understand the content (Camiciottoli, 2005; Lee, 2011; Miller, 2007; Nisbet & Spooner, 1999). Furthermore, one of the most common concerns reported in relation to online learning literature is the poor audio quality due to restricted internet bandwidth availability and traffic congestion (Chen et al., 2005; Wang et al., 2008). These problems can hinder students' understanding of a delivered speech, and this may hamper students from engaging in classroom participation and interaction (Camiciottoli, 2005; Miller, 2007; Chen et al., 2005; Wang et al., 2008). According to related literature, abovementioned problems can be solved by adopting some assistive media-to-text recognition technologies, such as writing-to-text, image-to-text, diagram-to-text, text-to-speech, speech-to-text, and handwriting-to-text. For example, Speech-to-Text Recognition (STR) technology synchronously transcribes text streams from speech input and shows them on a whiteboard or students' computer screens (Alapetite, Andersen, & Hertzum, 2009; Fichten et al., 2000; Hwang, Shadiev, Kuo, & Chen, 2012; Jones, 2005; Konur, 2007; Kuo, Shadiev, Hwang, & Chen, 2012; Shadiev, Hwang, & Huang, 2013). It is suggested that STR-generated texts can greatly help students attain a better understanding of a lecture, do simultaneous note-taking during lectures, and complete homework (Hwang et al., 2012; Kuo et al., 2012; Shadiev et al., 2013). Furthermore, it is argued that STR-generated text can be employed as an additional text confirmation of what is being said, and it aids comprehension in case when listeners are students with learning or physical disabilities, foreign students, and other at risk populations (Shadiev et al., 2013; Wald & Bain, 2008). The pedagogical usefulness of STR-technology application to enhance students' learning was emphasized in several studies. The following are a few examples. The Speech Recognition in Schools Project (Nisbet & Wilson, 2002; Nisbet, Wilson, & Aitken, 2005) helped students to overcome difficulties in reading, writing, and spelling. The project presented significant improvements in some students' basic reading, writing, and spelling skills with the support of STR. Wald and Bain (2008) developed STR applications to assist deaf students and non-native speakers to be involved in lectures. According to their research, students perceived that text generated by STR could improve learning if its accuracy is fairly good (Colwell, Jelfs, & Mallett, 2005; Wald & Bain, 2008). Ryba et al. (2006) examined the application of STR in a university lecture theatre attended by students who were native and non-native speakers of English. A non-native English lecturer delivered a course about information system to the participants by using STR. The participants reported that the system was a potentially useful instructional support mechanism; however, a greater accuracy in lecture text vocabulary recognition of the system needs to be achieved. Shadiev et al. (2013) applied STR technology to assist non-native English participants to learn at a seminar in English. It was found that most participants perceived that transcripts were useful for learning. Moreover, nineteen learning strategies to use transcripts were discovered, and participants with different learning achievements demonstrated different learning behaviors to use transcripts. …