Perceptual category mapping between English and Korean prevocalic obstruents: Evidence from mapping effects in second language identification skills

Abstract

The current study develops an approach to quantify the extent to which native language (L1) categories are used in second language (L2) category identification, and uses this approach to examine the identification of a set of English obstruents by Korean learners of English as a foreign language. Forty native Koreans listened to nonsense English CV words consisting of /p b t d f v θ ð/ and /a/, and were asked to identify the consonant with both Korean and English labeling. They also gave gradient evaluations of the goodness of the Korean labels to the stimuli. The results of the Korean labeling task were analyzed to predict what confusion patterns would be expected if listeners used L1 categories and probabilistically mapped them onto L1 category responses. Results show the perceptual patterns of L2 stops can be successfully predicted by use of L1 categories alone if the listeners’ goodness rating scores were used to weight the probabilistic mapping from L1 to L2 in the predictions. Accuracy for other segments, such as /p/ and /f/, was higher than predicted. In general, this increase in accuracy over what is predicted from the L1 mapping data was negatively correlated with the average goodness-of-fit to the Korean. These results provide quantitative corroboration of acquisition models claiming that some L2 categories can function by using existing L1 categories alone while others must be indicative of the addition of a new linguistic category.

Introduction

A fundamental tenet of Flege's Speech Learning Model (SLM: Flege (1987), Flege (1995)) is that the degree of success an acquirer will have in approximating the production of a non-native speech segment depends on the perceptual similarity of that segment to segments in the native language. Specifically, when an L1 and an L2 segment are similar enough to each other, the L2 segment can be functionally approximated more quickly than less similar segments. However, over time, these less similar or “new” sounds will be acquired more accurately, since a learner is said to construct an L2 category de novo. The more similar segments, by contrast, are entangled heavily with existing L1 categories, and so retain aspects of the L1 categories long into the acquisition process.

The basic role of cross-language perceptual similarity in L2 perception is also commonly accepted. It is a fundamental tenet of Best and her colleagues’ Perceptual Assimilation Model (PAM: Best, McRoberts, & Goodell, 2001; Best, McRoberts, & Sithole, 1988), that a listener's initial perceptual ability to discriminate sounds also depends on the perceptual similarity between the objects of perception in an L2 and those in an L1. Using a general technique similar to classic research in the categorical perception literature, research in PAM typically uses a cross-language similarity mapping task targeting contrasting pairs of segments in an unfamiliar language, and then uses the results of such higher level tasks to classify the particular pairs with respect to various scenarios. Based on this classification, researchers then make qualitative predictions about listeners’ lower-level abilities to discriminate the sounds. This research has accrued much evidence that discrimination abilities depend not just on familiarity with sounds similar to those in the L2, but also on the degree to which the listeners’ L1 categories require them to fail to differentiate the sounds in linguistic categorization; discrimination is very good both in cases in which the L1 categories require the listeners to differentiate the sounds (i.e., Two Category assimilation), and in cases where one of the sounds falls outside of the L1 system (i.e., Uncategorized–Categorized pair), and thus is poorly assimilated with any category in L1. Cases in which two sounds map onto a single L1 category, and so are not differentiated in L1 at all (i.e., Single Category assimilation), yield the poorest discrimination abilities. Though the two sounds map onto a single L1 category, in cases in which one sound fits into the L1 category better than the other sound (i.e., Category Goodness difference), moderately good discrimination is expected.

Guion, Flege, Akahane-Yamada, and Pruitt (2000) extend this research approach in cross-language perception to the learners who are the object of the SLM model, finding generally similar results. Examining a variety of segmental pairs (or triplets) in English that typically exhibit substitution effects in Japanese learners of English, Japanese listeners performed mapping tasks in which they labeled English productions with Japanese orthographic characters and gave goodness-of-fit ratings on a Likert scale. These results were then used to classify pairs of segments according to PAM scenarios, finding essentially the same results as earlier cross-language perception studies. Extending these results to listeners with more experience with English showed that the time-course of development could not be readily predicted from the cross-language mapping data, suggesting that, while research in the SLM has accrued much evidence for the role of similarity between segments in L2 production accuracy, predicting learning patterns will require more than similarity mapping.

The SLM differs from PAM in that it seeks to determine the extent to which a single L2 category is native-like, while PAM examines relations between categories. While much previous research has investigated mapping and discrimination (Best et al., 2001; Flege & MacKay, 2004; Polka, 1995; Tsukada et al., 2005), there is little research examining quantitatively the relationship between mapping and identification. While identification skills require discrimination skills, learning a linguistic function requires more than just discrimination, but also robust association to the right linguistic categories. When dealing with learners with a variety of ambient experience with the L2, identification performance can indicate explicitly how the listeners are functionally differentiating speech objects. Much like earlier SLM research, the current research seeks to predict identification abilities in learners on the basis of L1-to-L2 orthographic mapping patterns. However, unlike previous work on identification, the current study seeks to quantify the relationship between mapping results and identification.

The current research investigates and seeks to quantify the perceptual similarity between consonants in Korean and English, and uses these similarity estimates to make quantitative predictions about L2 identification skills. Similar to the predictions of PAM, the basic hypothesis about L2 identification accuracy is that cases in which native L2 productions of two contrasting segments are mapped onto the same L1 category will yield confusions between the two L2 sounds, and so are particularly problematic for L2 identification. By contrast, cases in which L2 productions of two contrasting segments are systematically mapped onto different L1 categories will not be confusing, even if there is no single L1 category corresponding to either of the L2 categories. Similar to the predictions of SLM about production, a second hypothesis is that L2 segments that do not map well onto any L1 category will not present L2 identification problems for learners with enough experience with the L2 to have acquired the segmental label.

Schmidt (1996) provides groundwork for Korean-to-English mapping. Schmidt asked Korean learners of English to attempt to label English consonant productions using Korean orthographic characters. The orthographic classification technique, in which a phonemic L1 orthographic system is used for the response labels, allows learners to use the very familiar L1 labels to tap into their native Korean and apply the native categories to the variety of consonants in English. Several researchers have used this orthographic classification technique in cross-language perception work. Wiik (1965) investigated the vowel space of English and Finnish with this method. Since Finnish has a phonemic orthographic system with a one-to-one correspondence between phonemic and graphemic categories, listeners’ responses show which English vowel corresponds to which Finnish vowels. He asked Finnish listeners to write what they heard on an answer form with ordinary Finnish orthography, and he analyzed how probable each English vowel was to be identified as each Finnish vowel. Flege (1991) also used this technique for investigating Spanish and English vowels. Because the Spanish orthographic system for vowels also has a one-to-one grapheme-to-phoneme mapping, he asked native Spanish listeners to label English vowels with one of the letters of the five vowel phonemes of Spanish (i.e., /i/, /e/, /o/, /a/, and /u/). More recently, Cebrian (2006) investigated the mappings between English and Catalan high and mid front vowels. He asked native speakers of Catalan to choose one of four Catalan orthographic representations, namely, ‘i’, ‘è’, ‘é’, and ‘ei’ (/i/, /e/, /ε/, /ei/, respectively), as the best matching Catalan vowel for an English vowel stimulus.

One potential problem with these studies, however, is that the orthographic probes used in both languages are the same. Finnish, Spanish, Catalan, and English all have Roman character writing sets. Confusion may arise between the phonemic and orthographic levels when studying the languages sharing the same alphabet (Park, de Jong, & Silbert, 2004); responses using L1 labels could be occurring at the level of orthography rather than at the level of auditory categorization. However, this is not an issue for languages that do not share characters in their orthographic systems. A second potential complication also arises in studies such as Guion et al. (2000), which uses Roman and Japanese orthography, in that the Japanese system does not incorporate segmental labels, but requires the listeners to make syllable-level judgments about the stimuli to assign orthographic labels.

Kim (1972) examined Korean and English consonants using Korean orthography. Since the Korean orthography is alphabetic, but shares no character structure with the English orthographic system, character usage is not an issue. Schmidt (1996) replicated Kim's study with more controlled experimental variables. She asked 20 native Korean listeners to type the initial consonant they heard when listening to English stimuli with Korean orthography. The stimuli were nonsense CV syllables of 22 consonants combined with vowels /i a u/ spoken by three female native English speakers. She also asked the listeners to judge how similar that English sound was to the selected Korean orthographic response using a Likert scale from 1 to 5. Schmidt (1996), however, did not obtain English identification performance from her listeners. The current research partially replicates Schmidt (1996), and also seeks to determine the degree to which such L2-to-L1 mappings are implicated in L2 perceptual identification.

We examined the Korean listeners’ perception of English anterior obstruents using the orthographic classification technique. We also used a rating of each response with a Likert scale in order to assess the strength of the mapping between the stimuli and the Korean labels (Best et al., 1988; Cebrian, 2006; Guion et al., 2000; Polka, 1995; Schmidt, 1996; Strange et al., 1998; Tsukada et al., 2005). From these mapping data, we seek to predict English identification accuracy, using a model where identification is based entirely on the listeners’ L1 categories and the mapping of these categories onto L2 labels. The success of these predictions indicates the degree to which L2 identification performance can be said to be due to the use of L1 categories. Increments in performance beyond such a hypothetical baseline, then, are taken to indicate the development of new L2 categories.