Transparent /aɪ/-raising as a contact phenomenon

Canadian Raising is a phonological process which raises the nucleus of both the /aɪ/ and /aʊ/ diphthongs above 60Hz (Labov et al. 2005: ANAE, p. 205) before voiceless segments. The /aɪ/ diphthong is raised in much of Canada as well as in many American dialects, including the Inland North, resulting in alternations among a large number of minimal pairs distinguished by their voicing such as /lʌɪf/ ‘life’ ∼ /laɪv/ ‘live’ and /brʌɪt/ ‘bright’ ∼ /braɪd/ ‘bride.\u27 This /aɪ/-raising is a classic example of phonological opacity because it is canonically conditioned not only by surface voiceless segments but also underlyingly voiceless segments, as with the flapped /t/ in /rʌɪɾɚ/ ‘writer.’ However, not all /aɪ/-raising speakers exhibit this opaque pattern: so-called transparent or phonetic /aɪ/-raising speakers only raise before surface voiceless segments as in /rʌɪt/ ‘write\u27 but not /raɪɾɚ/ ‘writer.’ The existence of this latter group has renewed debate about the ultimate origins of the raising patterns and the relationship between transparent and canonical raising. This paper contributes to that discussion with a new model of child language acquisition in variable settings, finding that the presence of transparent /aɪ/-raising as well as its rare attestation and sparse distribution can be accounted for as a contact phenomenon in which some child learners innovate a novel transparent raising pattern when their communities contain the appropriate mix of canonical raising and non-raising speakers

Alternation-Sensitive Phoneme Learning: Implications For Children\u27s Development And Language Change

This dissertation develops a cognitive model describing when children learn to group distinct sound segments (allophones) into abstract equivalence classes (phonemes). The allophones an individual acquires are arbitrary and determined by their particular input, yet are intricately involved in language cognition once learned. The proposed acquisition model characterises the role of surface segment alternations in children\u27s input by using the Tolerance Principle (Yang 2016) to evaluate the cognitive cost of possible phoneme inventory structures iteratively as a child’s vocabulary grows. This Alternation-sensitive Phoneme Learning model therefore traces the emergence of abstract representations from concrete speech stimuli, starting from a default representation where underlying contrasts simply mirror surface-segment contrasts (Invariant Transparency Hypothesis, Ringe & Eska 2013). A longitudinal corpus study of four children\u27s alveolar stop and flap productions establishes that English medial flap allophony follows a U-shaped acquisition course, which is characteristic of learning linguistic rules or generalisations. The Alternation-sensitive Phoneme Learning cognitive model is then validated by accurately predicting the timing of changes in each child\u27s productions, which signal allophone acquisition. A second case study models the historical process of secondary split in Menominee mid and high back vowels. Here, the acquisition model serves as an independently motivated quantitative test for the occurrence of phonemic split, providing an alternative to traditional reliance on linguists\u27 case-specific subjective judgements about when it might occur. A third case study examines the phonemic status of the velar nasal in German, showing how this acquisition model can discriminate between tolerable grammars and the subset of tolerable grammars that are learnable, with implications for the relationship between formal language description and psychological representation. This dissertation\u27s approach synthesises insights from computational modelling, naturalistic corpus data, historical linguistics, and experimental research on child language acquisition

A New Acoustic-Based Pronunciation Distance Measure

We present an acoustic distance measure for comparing pronunciations, and apply the measure to assess foreign accent strength in American-English by comparing speech of non-native American-English speakers to a collection of native American-English speakers. An acoustic-only measure is valuable as it does not require the time-consuming and error-prone process of phonetically transcribing speech samples which is necessary for current edit distance-based approaches. We minimize speaker variability in the data set by employing speaker-based cepstral mean and variance normalization, and compute word-based acoustic distances using the dynamic time warping algorithm. Our results indicate a strong correlation of r = −0.71 (p < 0.0001) between the acoustic distances and human judgments of native-likeness provided by more than 1,100 native American-English raters. Therefore, the convenient acoustic measure performs only slightly lower than the state-of-the-art transcription-based performance of r = −0.77. We also report the results of several small experiments which show that the acoustic measure is not only sensitive to segmental differences, but also to intonational differences and durational differences. However, it is not immune to unwanted differences caused by using a different recording device

Neural representations for modeling variation in speech

Variation in speech is often quantified by comparing phonetic transcriptions of the same utterance. However, manually transcribing speech is time-consuming and error prone. As an alternative, therefore, we investigate the extraction of acoustic embeddings from several self-supervised neural models. We use these representations to compute word-based pronunciation differences between non-native and native speakers of English, and between Norwegian dialect speakers. For comparison with several earlier studies, we evaluate how well these differences match human perception by comparing them with available human judgements of similarity. We show that speech representations extracted from a specific type of neural model (i.e. Transformers) lead to a better match with human perception than two earlier approaches on the basis of phonetic transcriptions and MFCC-based acoustic features. We furthermore find that features from the neural models can generally best be extracted from one of the middle hidden layers than from the final layer. We also demonstrate that neural speech representations not only capture segmental differences, but also intonational and durational differences that cannot adequately be represented by a set of discrete symbols used in phonetic transcriptions.Comment: Submitted to Journal of Phonetic

Ecological and Genomic Attributes of Novel Bacterial Taxa That Thrive in Subsurface Soil Horizons.

While most bacterial and archaeal taxa living in surface soils remain undescribed, this problem is exacerbated in deeper soils, owing to the unique oligotrophic conditions found in the subsurface. Additionally, previous studies of soil microbiomes have focused almost exclusively on surface soils, even though the microbes living in deeper soils also play critical roles in a wide range of biogeochemical processes. We examined soils collected from 20 distinct profiles across the United States to characterize the bacterial and archaeal communities that live in subsurface soils and to determine whether there are consistent changes in soil microbial communities with depth across a wide range of soil and environmental conditions. We found that bacterial and archaeal diversity generally decreased with depth, as did the degree of similarity of microbial communities to those found in surface horizons. We observed five phyla that consistently increased in relative abundance with depth across our soil profiles: Chloroflexi, Nitrospirae, Euryarchaeota, and candidate phyla GAL15 and Dormibacteraeota (formerly AD3). Leveraging the unusually high abundance of Dormibacteraeota at depth, we assembled genomes representative of this candidate phylum and identified traits that are likely to be beneficial in low-nutrient environments, including the synthesis and storage of carbohydrates, the potential to use carbon monoxide (CO) as a supplemental energy source, and the ability to form spores. Together these attributes likely allow members of the candidate phylum Dormibacteraeota to flourish in deeper soils and provide insight into the survival and growth strategies employed by the microbes that thrive in oligotrophic soil environments.IMPORTANCE Soil profiles are rarely homogeneous. Resource availability and microbial abundances typically decrease with soil depth, but microbes found in deeper horizons are still important components of terrestrial ecosystems. By studying 20 soil profiles across the United States, we documented consistent changes in soil bacterial and archaeal communities with depth. Deeper soils harbored communities distinct from those of the more commonly studied surface horizons. Most notably, we found that the candidate phylum Dormibacteraeota (formerly AD3) was often dominant in subsurface soils, and we used genomes from uncultivated members of this group to identify why these taxa are able to thrive in such resource-limited environments. Simply digging deeper into soil can reveal a surprising number of novel microbes with unique adaptations to oligotrophic subsurface conditions