This study investigates the low back vowel merger (lot-thought merger) of African American and European American speakers in Washington DC. The study aims to follow up with the previous investigation by Labov et al. (2006) on low back merger in DC, and investigate whether African American speakers in DC are participating in the merger, one of the majority-led sound changes. The study also examines the difference in low back vowel production between African Americans from a particular quadrant of the city, namely, Southeast (SE), and those from elsewhere in DC. Data are taken from forty sociolinguistic interviews with natives of DC. The vowels were analyzed acoustically, taking F1 and F2 measurements at three different points of a vowel. Both the F1 and the F2 dimensions of the low back vowels and the degree of overlap were examined. The degree of overlap was measured by calculating the Pillai score for each speaker, with duration and following environment taken into account. Results indicate that DC is conservatively participating in the merger, with European American speakers exhibiting higher degrees of overlap than African American speakers. An age effect is also observed, but only among African American speakers who are not from SE. The study further argues that the participation in the mainstream sound change by some African Americans can also be analyzed as convergence to the local white norm, and that this convergence is more likely to be carried out by African American speakers who are not from SE, a section of the city in which the contact between African American speakers and European American speakers is minimal.

This study examines the relationship between speech characteristics and personality traits by drawing on pseudo-naturalistic conversations and on personality dimensions identified by the Myers–Briggs Type Indicator (MBTI) model which assesses four personality dimensions of introversion-extroversion, sensing-intuiting, thinking-feeling, and judging-perceiving. The speech of 30 participants was recorded and transcribed, after which a number of speech features including pitch, loudness, response time (i.e., how fast one responds to a prompt), speech rate, and discourse markers were extracted and analyzed. Results show that several speech features correspond to different personality dimensions. Specifically, speech rate as measured by words per minute reveals significant differences between judging individuals and perceiving individuals (perceiving individuals speak faster than judging individuals); there is a significant difference in response time for extroverts and introverts (extroverts respond faster); a significant difference is observed in loudness between judging and perceiving individuals (judging individuals are louder). The frequency of discourse markers is significantly higher for intuiting individuals than sensing individuals. The study discusses these findings in further inquiring the relationship between language and personality.

According to the similarity-attraction theory, humans respond more positively to people who are similar in personality. This observation also holds true between humans and robots, as shown by recent studies that examined human-robot interactions. Thus, it would be conducive for robots to be able to capture the user personality and adjust the interactional patterns accordingly. The present study is intended to identify significant speech characteristics such as sound and lexical features between the two different personality groups (introverts vs. extroverts), so that a robot can distinguish a user’s personality by observing specific speech characteristics. Twenty-four male participants took the Myers-Briggs Type Indicator (MBTI) test for personality screening. The speech data of those participants (identified as 12 introvertive males and 12 extroversive males through the MBTI test) were recorded while they were verbally responding to the eight Walk-in-the-Wood questions. After that, speech, sound, and lexical features were extracted. Averaged reaction time (1.200 s for introversive and 0.762 s for extroversive; p = 0.01) and total reaction time (9.39 s for introversive and 6.10 s for extroversive; p = 0.008) showed significant differences between the two groups. However, averaged pitch frequency, sound power, and lexical features did not show significant differences between the two groups. A binary logistic regression developed to classify two different personalities showed 70.8% of classification accuracy. Significant speech features between introversive and extroversive individuals have been identified, and a personality classification model has been developed. The identified features would be applicable for designing or programming a social robot to promote human-robot interaction by matching the robot’s behaviors toward a user’s personality estimated.