Research Background

Speech rate (SR) is defined as “the speed of message delivery” (Pöchhacker, 2004, p.129). SR is a determinant factor of listening comprehension of a speech (Kopparapu, 2013; Rodero, 2012). That is, SRs that are either too fast or too slow can both adversely impact listeners’ understanding. While slower SRs generally provide more time for listeners to process information (Megehee, Dobie, & Grant 2003), SRs that are too slow are hard to retain attention on (Rodero, 2012) and cause difficulty in comprehension (Peltekov, 2017). On the other hand, very fast SRs are hard to keep up, too, because too many words are presented within a short amount of time without enough pauses for listeners to process and understand the message the speaker wants to convey (Nikitina, 2011; Rodero, 2012). To enable listeners to fully understand the message, it is best for speakers to speak at an optimal speech rate (Munro & Derwing, 2001). The optimal rate of public speaking in English, according to Arina Nikitina, is around 140-150 word per minute (wpm) (Nikitina, 2011).

To conference interpreters, fast speech rate (FSR) is known to be especially

challenging. In the Statistical World Report 2009 by AIIC, fast speeches were identified as the biggest stressor felt by interpreters (Neff, 2011). FSR is difficult to cope with because it increases processing capacity requirements for interpreting (Gile, 2009). In Gile’s Effort Models, different Efforts were introduced as processing capacity demands on interpreters. Each Effort has its requirements on interpreters’ attention, and thus

interpreters only have a limited supply of processing capacity when performing

interpretation; therefore, when one or more of these Efforts require greater capacity than available, the total capacity at the interpreter’s disposal may be insufficient, thus

resulting in a regression in the interpreter’s output performance. Exceedingly fast SR means that more information will have to be processed in the same amount of time, which will lead to the afore-mentioned elevated capacity requirements. Since an interpreter’s attention is allocated to meet the requirements of each Effort, which is different from only listening and understanding a speech, the optimal rate of input for interpretation is lower than that for just listening and comprehending (Vančura, 2013).

This is probably why according to Setton and Dawrant (2016b), 120-140 wpm is considered a “moderate” SR for interpreters, while 140-160 wpm is considered

“challenging.”¹

If SR exceeds a certain limit, interpreters will not be able to render the message completely (Li, 2010). This can happen to interpreters performing simultaneous and consecutive interpreting alike (Chang, 2009; Gerver, 2002 [1969]; Ribas, 2012; Wu et al., 2010). In the context of consecutive interpreting (CI), a few experimental studies show that when interpreters encounter a FSR, accuracy decreases, omission increases, and summarization is used more (Ribas, 2012; Wu et al., 2010). This suggests that FSR makes it challenging for CI interpreters to keep every message full and complete until output production. Some researchers have categorized CI into several phases. For example, Giles’ Effort Model, when applied for CI, contains a “listening and

1 Setton and Sawrant (2016b) pointed out that measuring SR by wpm may yield different results for different languages. Though it was not specified in Setton and Sawrant to which languages do these measurements apply, it can be inferred that they can at least be applied to English.

note-taking” phase and a “target-speech production” phase (2009, p. 175). In the first phase, the interpreter has to exert Efforts in “listening and analysis,” “note-taking,”

“short-term memory operations,” and “coordination.” In the second phase, Efforts include “remembering,” “note-reading,” and “production.” Liu (2008, p. 19) proposed a similar model, categorizing the procedures of CI into “listening,” “analysis and

comprehension,” and “short-term memory operations and/or note-taking” in the first phase, and “remembering and/or note-reading” and “production” in the second phase.

Wu et al. (2010) stated that since every step in CI is connected, a fast speaker will have a chain effect, resulting in the interpreter’s failure to render complete and/or accurate messages.

One of the important parts in CI is note-taking. According to Gile (2009), different Efforts compete with each other for the limited processing capacity available in

interpretation. Note-taking and listening are both performed in the first phase of CI, in which focus of attention shifts between listening and note-taking at a very fast pace (Liu, 2008). Taking notes occupies a certain amount of processing capacity; therefore, it will more or less affect the listening Effort (Liu, 2008). However, note-taking can be an effective tool in CI if used effectively, because for long CI, the length of each interpreting segment is usually longer, which makes it difficult for interpreters to memorize all the message (Liu, 2008). CI notes help interpreters to remember messages from the speech, serving as “memory reinforcers” (Gile 2009, p. 178) or “visual cues”

(Liu, 2008, p. 27). Effective notes may boost “remembering” operations and lower required processing capacity for “remembering” in the second phase of CI (Gile, 2009,

p. 176). However, poor notes will hamper Efforts needed in the second phase of CI.

Thus, note-taking effectiveness can potentially make or break a CI performance.

According to Daniel Gile’s observations, CI is commonly used in Asia’s

interpreting markets (Gile, 2001). In Taiwan, in particular, CI has been one of the most common modes of interpretation for both freelance and in-house interpreters. According to a survey conducted by Government Information Office (GIO), Executive Yuan (2004), among all the interpretation work done by freelance interpreters in Taiwan in 2001, the ratio of CI to SI was 38:57, while whispering was much less frequent than the other two modes. In addition, a survey on freelance translators and interpreters in 2012 shows that among all the interpreting assignments, CI was the most frequently used mode of interpretation, and 27.63% of the surveyed interpreters claimed that CI accounted for over 81% of all of their interpreting assignments (Chen et al., 2012). As for in-house interpreters, among all the interpreting assignments, whispering and CI are reported to be the most common (Li, 2016). In view of the important role note-taking plays in CI as previously mentioned, as well as the prevalent use of CI in Taiwan, it makes sense to investigate into note-taking strategies to cope with FSR.

Nevertheless, research on this issue is sparse. Past research has focused on CI note-taking skills and strategies, or strategies to deal with the challenge of FSR in SI.

The former, for instance, includes the early study by Rozan (1956) in which a note-taking system was proposed, and more recently, studies by Gillies (2005) and Chuang (2008), in which note-taking guidelines and techniques were introduced. Recent studies of CI note-taking have focused on the relation between note-taking preferences

and CI performance. Most of them investigated into note quantity, symbols and abbreviations, or source and target languages or A and B languages in general CI occasions. As for how to deal with FSR, Barghout, Rosendo, and García (2015), Li (2010), and Setton and Dawrant (2016b) listed various approaches in the context of SI.

Some strategies, such as summarization, can possibly be applied to CI and note-taking.

However, other strategies such as speeding up in output production and switching off the microphone are not entirely applicable to CI without some modifications. Overall, to date, no study has been done specifically on finding possible note-taking strategies for coping with FSR in CI. In this study, the researcher intends to fill the gap, and propose some possible note-taking strategies that could be adopted in coping with FSR.