There are multiple acoustic dimensions that define speech categories. During spoken language comprehension, listeners categorize speech sounds based on these continuous acoustic cues. Listeners need to determine which cues are relevant to pay attention to, and what relative importance each cue has in order to assign more weight to that cue. Several studies have attempted to find the acoustic dimensions that are important in the discrimination of different speech sounds. Morrison (2013) provides a review of theories related to dynamic aspects of vowel perception. Strange and Jenkins (2013), in their Dynamic Specification model, mention that the most important cues to vowel identity are in the spectro-temporal patterns of consonant–vowel and vowel–consonant formant transitions.

Among the early studies on the role of different cues in the perception of vowels is the study by Bennett (1968), which investigated the relative importance of the spectral and temporal cues in the discrimination of pairs of English and German vowels. He suggested that the importance of the temporal cue is inversely proportional to the distance between the qualities of a given pair of vowels. His results showed that spectral form is, in general, more important than duration in vowel recognition in both English and German, and it is only when two vowels are very close in quality that the duration cue is more important for their discrimination. Ainsworth (1972) used sets of synthetic vowel sounds that differed in first-formant frequency, second-formant frequency, and duration, and investigated the effect of these cues on the identification of vowels. He found that listeners’ judgments depended on all of these factors; however, duration was a relatively more important cue for vowels located in the centre of the f ₁–f ₂ space where a vowel might more readily be confused with one of its neighbours.

According to Idemaru et al. (2012), “whereas any of the acoustic dimensions may play a role in phonetic categorization, they are not necessarily perceptually equivalent”. Giving greater perceptual weight to some of the acoustic dimensions is referred to as cue weighting (Holt & Lotto, 2006; Francis, Kaganovich, & Driscoll-Huber, 2008; Idemaru et al., 2012). Hillenbrand, Clark, and Houde (2000) found that English listeners give more weight to the spectral than the temporal dimension in categorizing English [i] and [ɪ] vowels. It has also been found that in discrimination of voiced and voiceless bilabial stop consonants at the syllable initial position, voice onset time (VOT) is more strongly weighted by English listeners and fundamental frequency (f ₀) of the following vowel is used as a secondary cue in the discrimination of this pair (Abramson & Lisker, 1985; Francis et al., 2008). Holt and Lotto (2006) suggest that dimensions that are highly related to category identity need to be more strongly perceptually weighted than those less predictive of category identity. These acoustic dimensions are sometimes weighted differently among listeners.

There is not extensive research on individual differences in cue-weighting strategies in speech perception (see, e.g., Allen, Miller, & DeSteno, 2003; Haggard, Ambler, & Callow, 1970; Hazan & Rosen, 1991; Idemaru, Holt, & Seltman, 2012; Kong & Edwards, 2011, 2016; Raizada, Tsao, Liu, & Kuhl, 2010; Shultz, Francis, & Llanos, 2012). Some studies have shown that individual variability exists in cue-weighting strategies (Haggard et al., 1970; Hazan & Rosen, 1991; Idemaru et al., 2012). For instance, Shultz et al. (2012) reported individual differences in the extent of reliance on the secondary cue (f ₀) in discrimination of /b/ and /p/. Idemaru et al. (2012) suggest that examining individual differences in perceptual cue weighting in a situation where different dimensions provide similar informativeness provides an opportunity to better understand listeners’ sensitivity to distributional characteristics of acoustic dimensions in categorization of speech sounds. For instance, Chládková, Hamann, Williams, and Hellmuth (2016) found that f ₂ slope direction is used as a cue (additional to midpoint formant values) to distinguish /iː/ from /uː/ by British English listeners.

A series of G² comparisons as described in the method section were performed to examine which of the cues were used significantly for vowel categorization. Inclusion of f ₂ significantly improved the fit of the model for 20 out of 20 participants (p < 0.05), compared to a model without any independent variable; when only f ₃ was included, the fit of the model significantly improved for 9 participants out of 20 (p < 0.05), compared to a model without any factor. Figure 6 represents a scatterplot for the coefficients of the regression model with f ₂ and f ₃ for the 20 participants. Escudero et al. (2009) mention that “the coefficients of the logistic regression analysis show to what extent a one-step difference in one of the predictors causes a change in the log odds of a participant’s response (p. 457)”.

As described in 2.4, a cue weighting of 0.5 indicates that the listener weights both cues equally, and a cue weighting higher than 0.5 indicates that the weighting of f ₂ is heavier than that of f ₃; if it is below 0.5, f ₃ is weighted heavier. Figure 7 represents the mean cue weighting for each of the participants. The closer the coefficients of f ₂ and f ₃ (Figure 6) are to each other, the closer the relative cue weighting is to the center line (both cues weighted equally; Figure 7).

According to Figure 7, most of the listeners weighted f ₂ heavier, and some listeners weighted f ₃ heavier or both equally. However, there are differences on the amount of weighting of cues among the listeners. Overall, the reliance on f ₂ was much stronger than on f ₃. Some participants’ relative cue-weighting scores were close to 1 (f ₂ only); however, the three participants who gave more weight to f ₃ did not weight it as strong as the average of participants who gave more weight to f ₂.

Finally, we computed the polar-coordinate magnitudes of the two models (only f ₂ and f ₂ + f ₃) and compared the models’ steepness in categorization boundaries. As mentioned by Morrison (2007),

Figure 8 shows the probability of choosing the /œ/ vowel along the 8 steps. Compared to the model when only f ₂ changes toward the /œ/ vowel, changes of both in f ₂ and f ₃ make the probability of /œ/ response to jump steeper toward 1.

There was a significance difference between the coefficients of these two models (t = -2.03, p = 0.05), and inclusion of f ₃ made the curve steeper compared to model with only f ₂ (Figure 8). The mean polar-magnitude values for models were f ₂ + f ₃ = 0.83 > f ₂ = 0.74.

The current study examined perceptual weighting of different acoustic dimensions in perceptual discrimination of Azerbaijani /œ/ and /ɯ/ vowels. Regarding the large overlap in f ₁– f ₂ vowel space in the production of these two vowels (Ghaffarvand Mokari & Werner, 2016), this study explored if other cues play a role in their discrimination. To the best of our knowledge, this is the first study of cue weight in the discrimination of native vowels based solely on spectral information. Our results revealed individual differences in the cue weighting for the perception of Azerbaijani /œ/ and /ɯ/ vowels. Although f ₂ was a more important cue for the discrimination of this vowel pair, f ₃ also played a role. Our results of the reliance on different cues revealed that a higher number of listeners mostly relied on f ₂ and some of them relied on f ₃ or on both.

Overall, one of the important findings in the present study is that f ₂ is still the main cue in the distinction of these two vowels despite their large overlap regarding their f ₂ values. One explanation for this issue would be that listeners use a perceptual vowel-intrinsic normalization process that does not need the information from other vowels. According to Adank et al. (2004) “vowel-intrinsic normalization models have been considered to be more suitable as models for human vowel perception” because they “can normalize a single vowel from a speaker without information about other vowels from that speaker” (p. 3105).

These findings are in line with individual differences reported in previous studies. Regarding the discrimination of voiced and voiceless stops, Stevens and Klatt (1974) report that some listeners relied more on VOT than f ₁ onset frequency, and Haggard et al. (1970) found that some listeners were more sensitive to f ₀ than VOT in distinguishing voiced and voiceless stops. More recently, Idemaru et al. (2012) found considerable variability between Japanese listeners’ perceptual weighting of absolute and relative durations in the discrimination of Japanese singleton and geminate stop categories.

In addition, our results revealed that the categorical boundary was steeper when both f ₂ and f ₃ were included in the model compared to the model including only f ₂. This was in line with the study by Hazan and Rosen (1991), who observed that listeners’ identification functions were uniformly steep in the full-cue condition.

Some previous studies have indicated that f ₂ and f ₃ might be perceptually regarded as one percept (f ₂ prime; Delattre et al., 1952; Fant & Risberg, 1963; Fox, Jacewicz, & Chang, 2011). Chistovich and Lublinskaya (1979) proposed that close formant peaks are merged into a single perceived spectral prominence. However, other studies suggest that a two-dimensional view on formants cannot explain vowel perception and at least three spectrally prominent regains (corresponding to f ₁, f ₂, and f ₃) are necessary to explain vowel perception (Fujimura, 1967; Rosner & Pickering, 1994). There is a need for further studies to investigate whether f ₂ and f ₃ covary in perceptual discrimination of Azerbaijani /œ/ and /ɯ/ vowels and whether they can be merged into one dimension in perception or not.

Francis et al. (2008) suggest that listeners normally rely on primary cues (e.g., on VOT in the discrimination of English stop voicing contrast) in ideal listening conditions. However, they adjust their cue weighting toward secondary cues under less-than-ideal conditions, for instance when listening to speech in noise or listening to multiple speakers. If f ₂ is the primary cue in the discrimination of the Azerbaijani /œ/ and /ɯ/ vowels, it can be assumed that listeners will use f ₃ cue in noisy and not in ideal conditions.

Our results also revealed individual differences in categorization gradiency in presence of different cues. In explanation of individual differences in speech perception, Kong and Edwards (2016) hypothesized that gradiency would be related to general cognitive control. They tested this hypothesis by correlating measures of gradiency with performance on measures of inhibition and task shifting and found little support for this claim. Kapnoula (2016) also did not find consistent relationships between gradiency and measures of executive function. Idemaru et al. (2012) speculate that their observed individual cue-weighting pattern can be due to the similar informativeness of the acoustic dimensions and it allows listeners to freely use either source of information, perhaps varying in which information they use across time.

Future research may look into the relation between production and perception of reliance on spectral cues. One would assume that the individuals who give more weight to f ₃ in discrimination of the Azerbaijani /œ/ and /ɯ/ vowels produce them also with heavier f ₃ differences. In summary, we observed some individual differences in cue-weighting strategies among native listeners. Although there are a few studies on the individual differences in cue weighting, the source of these differences still remains to be discovered in future.