We report six experiments on learnability of four non-adjacent phonotactic constraints which differ in their attested frequency and phonetic conditioning factors; liquid harmony, liquid disharmony, backness harmony, and backness disharmony. Our results suggest that such phonotactic constraints can be implicitly learned from brief experience and that learnability of a phonological grammar may be independent of its attested frequency and phonetic basis.
The English present perfect has received a great deal of sentence-based analytical attention in the empirical linguistic literature as well as some corpus-based coverage at the discourse level, in which analysis depends on close scrutiny of the context of a structure’s placement, i.e. several utterances, turns at talk, or sentences before and after. Due to the painstaking nature of such analysis, however, corpora used to date have been relatively small. This study, via a corpus of over 12 million words from television and radio interviews in the United States, categorizes the use of discourse-pragmatic functions of 268 present perfect tokens. From this analysis, one use stood out overwhelmingly: the present perfect in the employment of position-taking or support. Also, only very rarely was the present perfect used to initiate narratives, which is a finding that does not conform to previous understandings. The study contributes to the overall knowledge of present perfect use and has implications for how the tense can be taught to English language learners.
Bilingual speakers sometimes change their pitch and voice quality when they switch from one language to the other. For example, when speaking in L2 rather than L1, German learners of French pronounce vowels with less adduction of the vocal folds (Pützer et al., 2016), and Korean learners of English with a lower pitch (Cheng, 2020). Here, I present a corpus study which suggests that the extent to which L2 learners of English change their pitch and voice quality may depend on how similar their L1 is to English. I extracted 68 211 vowels—51 857 in L1 and 16 354 in L2—from 1617 speakers with 21 different L1 backgrounds (including English) in the CSLU: 22 Languages Corpus and measured F0, harmonics-to-noise ratio (HNR), and H1i–H2 for each vowel. I then computed two cluster distances for each L1 and for each measure: (1) vowels from the native English speakers versus L1 vowels from the learners and (2) L1 vowels versus L2 vowels from the learners. I found strong correlations between (1) and (2): r = 0.416 for F0 (p = 0.068), r = 0.531 for HNR (p = 0.016), and r = 0.374 for H1–H2 (p = 0.105).
Pattern playback systems were instrumental in speech perception research [e.g., Cooper et al. (1951)] and can be valuable for pedagogical purposes [e.g., Arai et al . (2006)]. They would be utilized further if one could integrate them with other speech processing software written in a common programming language. In response, I present an open-source digital pattern playback system implemented in the Python programming language. The software allows the user to provide an image of a magnitude spectrogram as input by either selecting an image file (e.g., PNG, JPG) or drawing one directly on a blank canvas using a pointing device (e.g., computer mouse, stylus, fingertip). It first translates pixel values of the image to an array of magnitude spectral coefficients and then applies the inverse short-time Fourier transform assuming zero phase to convert the array into a waveform. Users can readily manipulate basic parameters of conversion (e.g., sampling rate, frame length) and augment the process by utilizing various signal processing methods available in Python libraries such as SciPy and librosa. The source code is available for download and will be maintained on the author's GitHub repository and personal website.
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