Empirical studies of the process of interpreting

Just as earlier models of simultaneous interpreting focused on describing the process of interpreting, so have many empirical studies in interpreting focused on observing and quantifying parts of the process. This body of research has highlighted the difficulty of simultaneous interpreting, both by comparing it with other tasks such as shadowing, paraphrasing and consecutive interpreting, and by quantifying errors made in interpreting.

Further research has attempted to identify differences between the performance of interpreters and that of non-interpreters on an interpreting task. Finally, attempts have been made to examine how interpreters react to specific process-related difficulties, for instance rendering numerical data included in the source speech (Korpal & Stachowiak-Szymczak, 2018), speed of source text delivery (Barghout, Ruiz Rosendo, & Varela García, 2015; Ruiz-Rosendo & Galván, 2019) and mismatched word order in source and target language (Hodzik & Williams, 2017; Seeber, 2001; Wilss, 1978; discussed in section 1.9).

First of all, measuring the timings of input and output has shown that production of an utterance in simultaneous interpreting appears to take place shortly after its

comprehension, and that comprehension and production processes take place at the same time. The lag between input and output has been measured at approximately 4 to 5.7 words (Gerver, 1976), between 1900ms and 6000ms (Chernov, 1994) or 2 to 3s (Barik, 1973). Lag

times can vary greatly, including the lag times of the same interpreter during the same speech: Chernov (1994) found that lag time was 2.5 to 3s at the beginning of each passage, reaching up to 6s towards the end (of difficult passages) and then narrowing to as little as 0.5s. The fact that more difficult passages lead to a greater delay in production suggests that interpreters do not process all content in the same way. Timarová, Čeňková, Meylaerts, Hertog, Szmalec and Duyck (2014) also found that lag times varied significantly for each interpreter, but that more experienced interpreters tended to maintain a shorter lag (this may have been as a result of number-dense spoken texts, where accuracy may be improved by maintaining a shorter lag). Finally, interpreters make use of pauses in the original speech to produce more utterances than would otherwise be the case given their rhythm of

production, suggesting that simultaneous comprehension and production does come at a cost, even for experienced interpreters (Barik, 1973).

Researchers have investigated the difficulty of an interpreting task by comparing simultaneous interpreting with a similar task without the translation element (e.g.,

comprehension, shadowing, paraphrasing or articulatory suppression). Recall of content has been found to be affected by concurrent production, such that recall is worse after

shadowing than after listening (Gerver, 1974; Lambert, 1988), and worse after simultaneous interpreting than after listening (Daro & Fabbro, 1994; Gerver, 1974) and consecutive interpreting (Christoffels & De Groot, 2004). However, recall after simultaneous interpreting has been found to be better than recall after shadowing (Gerver, 1974; Lambert, 1988). The authors of these studies have generally concluded that concurrent production interferes with memory. However, these studies may also indicate that concurrent production interferes with comprehension.

On the other hand, some evidence suggests that the main source of complexity in simultaneous interpreting is producing the same message using different words, rather than the overlap of comprehension and production. Christoffels and De Groot (2004) compared lag, quality and recall in unbalanced bilinguals untrained in interpreting as they carried out interpreting, shadowing and paraphrasing tasks in a delayed or simultaneous condition. In the delayed condition, there was little difference between the performance measures for each task. In the simultaneous condition, performance measures for interpreting and paraphrasing were both poorer. Lag was longest for paraphrasing, and longer for

interpreting than for shadowing. This ties in with the model developed by Van Paridon et al.

(2019), which posits that a bottleneck prevents simultaneous lexical selection for

comprehension and production. The fact that lag was longest for paraphrasing rather than translating could be because between-language translation equivalents might be more easily accessed than within-language synonyms (Francis, 2005). In addition, paraphrasing requires that participants suppress a tendency to repeat, which may be difficult.

Dillinger (1994) investigated whether interpreters had better comprehension skills than bilinguals (with limited interpreting experience). He used production to measure comprehension in eight interpreters and eight bilinguals untrained in interpreting, and found that interpreters were more accurate in reproducing the source speech. He concluded that this was mainly due to better analysis of the message by interpreters (although, of course, this could also be due to superior production skills under pressure).

This provides evidence that although there is an overlap between interpreting and everyday bilingual communication, experience in interpreting specifically appears to lead to better

performance on an interpreting task (although this is of course an example of a small study with a between-participant design in which certain variables, e.g., age, were not controlled).

Research on errors and omissions in simultaneous interpreting (Barik, 1971) has shown that interpreting is a highly complex task. Even when experienced, interpreters omit information and make mistakes, suggesting that they find interpreting difficult. More recently, Plevoets and Defranq (2018) carried out a corpus analysis of transcribed interpretations from the European Parliament in order to identify particular sources of difficulty (measured by hesitations in the interpreted output). After defining potential predictors of difficulty, they considered the relative difficulty of different predictors. Two important predictors for more and less difficulty respectively were lexical density in the source text and formulaicity in the source and target text. Another analysis of speeches read at the UN Human Rights Council revealed that when source texts were read at higher

speeds, even experienced interpreters omitted information (Barghout et al., 2015), and a study of the effect of speed on target speech accuracy also showed that interpreters omitted more information at higher (source) speech rates (Ruiz-Rosendo & Galván, 2019).

Korpal and Stachowiak-Symczak (2018) found that a higher speed of source speech delivery led to lower accuracy in reporting numerical data, even among professional interpreters (although professionals were more accurate than student interpreters). These studies show that in certain conditions, interpreters do not appear to be able to comprehend and

produce the same content simultaneously.

These studies demonstrate the complexity of the simultaneous interpreting task, and show that even experienced simultaneous interpreters cannot guarantee perfect task

execution (although experience in interpreting leads to more accurate reproduction of a

source speech). Sources of additional difficulty in interpreting may include speed of source text delivery and lexical density of the source text, and difficulties of these types may lead to hesitations and greater lag times. More challenging comprehension conditions may thus limit concurrent production. In turn, this shows that the interpreting process may vary depending on content and speed.