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1. LINGUISTIC AND SOCIAL FACTORS FAVORING ACQUISITION OF CONTRAST IN A NEW DIALECT

   Nycz, Jennifer (January 2019, Proceedings of the 19th International Congress of Phonetic Sciences, Melbourne, Australia 2019)

   null (Ed.)

   This study describes linguistic and social factors favoring acquisition of a low back vowel contrast by native speakers of Canadian English living in New York City (NYC). Previous literature has found that new phonemic distinctions seem difficult to acquire, both in L2 and D2 (second dialect) learning con- texts. In contrast, this analysis shows that Canadian expats who have been exposed to NYC English due to mobility show small but significant distinctions between the COT and CAUGHT classes. Intriguingly, the social factor most strongly influencing the magnitude of this new contrast is not total years spent in NYC or even identification as a New Yorker, but choice of partner: Canadians married to New Yorkers show greater COT/CAUGHT contrast. These findings suggest that long term, consistent input from a regular and important interlocutor may facilitate the acquisition of new contrasts in a second dialect. 

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2. Large-scale acoustic analysis of dialectal and social factors in English /s/-retraction

   Stuart-Smith, Jane; Sonderegger, Morgan; Macdonald, Rachel; Mielke, Jeff; McAuliffe, Michael; Thomas, Erik R. (January 2019, International Congress of Phonetic Sciences)

   The retraction of /s/ in /str/, eg street, is a sound change found in certain English dialects. Previous work suggests that /s/-retraction arises from lower spectral frequency /s/ in /str/. The extent to which /s/-retraction differs across English dialects is unclear. This paper presents results from a large-scale, acoustic phonetic study of sibilants in 420 speakers, from 6 spontaneous speech corpora (9 dialects) of North American and Scottish English. Spectral Centre of Gravity was modelled from automatic measures of word-initial sibilants. Female speakers show higher frequency sibilants than males, but more so for /s/ than /ʃ/; /s/ is also higher in American than Canadian/Scottish dialects; /ʃ/ is surprisingly variable. /s/-retraction, modelled as retraction ratios, is generally greater for /str/ than /spr skr/, but varies by dialect; females show more retraction in /str/ than males. Dialectal and social factors clearly influence /s/-retraction in English clusters /sp st sk/, /spr skr/, and /str/. 

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3. The stories and words online regional dialect (SWORD) corpus

   https://doi.org/10.1121/2.0001942  

   Lilley, Kevin D; Ross, Jory P; Bissell, Marie; Clopper, Cynthia G (August 2024, Proceedings of meetings on acoustics)

   Online data collection allows for access to diverse populations. In the current study, we used online recruitment and data collection methods to obtain a corpus of read speech from adult talkers representing three authentic regional dialects of American English and one novel dialect created for the corpus. The authentic dialects (New England, Northern, and Southern American English) are each represented by 8–10 talkers, ranging in age from 22 to 75 years old. The novel dialect was produced by five Spanish-English bilinguals with training in linguistics, who were asked to produce Spanish /o/ in an otherwise English segmental context. One vowel contrast was selected for each dialect, in which the vowels within the contrast are acoustically more similar in the target dialect than in the other dialects. Each talker produced one familiar short story with 40 tokens of each vowel within the target contrast for their dialect, as well as a set of real words and nonwords that represent both the target vowel contrast for their dialect and the other three vowel contrasts for comparison across dialects. Preliminary acoustic analysis reveals both cross-dialect and within-dialect variability in the target vowel contrasts. The corpus materials are available to the scholarly community. 

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4. DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules

   https://doi.org/10.18653/v1/2023.emnlp-main.850  

   Liu, Yanchen; Held, William; Yang, Diyi (January 2023, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing)

   Existing large language models (LLMs) that mainly focus on Standard American English (SAE) often lead to significantly worse performance when being applied to other English dialects. While existing mitigations tackle discrepancies for individual target dialects, they assume access to high-accuracy dialect identification systems. The boundaries between dialects are inherently flexible, making it difficult to categorize language into discrete predefined categories. In this paper, we propose DADA (Dialect Adaptation via Dynamic Aggregation), a modular approach to imbue SAE-trained models with multi-dialectal robustness by composing adapters which handle specific linguistic features. The compositional architecture of DADA allows for both targeted adaptation to specific dialect variants and simultaneous adaptation to various dialects. We show that DADA is effective for both single task and instruction finetuned language models, offering an extensible and interpretable framework for adapting existing LLMs to different English dialects. 

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5. LPC Augment: an LPC-based ASR Data Augmentation Algorithm for Low and Zero-Resource Children’s Dialects

   https://doi.org/10.1109/ICASSP43922.2022.9746281  

   Johnson, Alexander; Fan, Ruchao; Morris, Robin; Alwan, Abeer (May 2022, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),)

   This paper proposes a novel linear prediction coding-based data augmentation method for children’s low and zero resource dialect ASR. The data augmentation procedure consists of perturbing the formant peaks of the LPC spectrum during LPC analysis and reconstruction. The method is evaluated on two novel children’s speech datasets with one containing California English from the Southern California Area and the other containing a mix of Southern American English and African American English from the Atlanta, Georgia area. We test the proposed method in training both an HMM-DNN system and an end-to-end system to show model-robustness and demonstrate that the algorithm improves ASR performance, especially for zero resource dialect children’s task, as compared to common data augmentation methods such as VTLP, Speed Perturbation, and SpecAugment. 

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