This paper discusses how the transcription hurdle in dialect corpus building can be cleared. While corpus analysis has strongly gained in popularity in linguistic research, dialect corpora are still relatively scarce. This scarcity can be attributed to several factors, one of which is the challenging nature of transcribing dialects, given a lack of both orthographic norms for many dialects and speech technological tools trained on dialect data. This paper addresses the questions (i) how dialects can be transcribed efficiently and (ii) whether speech technological tools can lighten the transcription work. These questions are tackled using the Southern Dutch dialects (SDDs) as case study, for which the usefulness of automatic speech recognition (ASR), respeaking, and forced alignment is considered. Tests with these tools indicate that dialects still constitute a major speech technological challenge. In the case of the SDDs, the decision was made to use speech technology only for the word-level segmentation of the audio files, as the transcription itself could not be sped up by ASR tools. The discussion does however indicate that the usefulness of ASR and other related tools for a dialect corpus project is strongly determined by the sound quality of the dialect recordings, the availability of statistical dialect-specific models, the degree of linguistic differentiation between the dialects and the standard language, and the goals the transcripts have to serve.

This investigation sought to establish nasalance scores for Jordanian Arabic (JA) speakers, to develop nasalance speech stimuli for JA speakers, and to compare JA adult nasalance scores with those of Standard American English (SAE) speakers.
Participants included normal native JA speakers (n = 100; males/females and adults/children) and adult SAE speakers (n = 49). Four speech tasks for JA speakers were developed, which included repetition of vowels, syllables, words, and sentences. Parallel speech tasks were also developed for SAE speakers. A nasometer (Nasometer II 6400; PENTAX Medical, Montvale, NJ, USA) was used to establish nasalance scores for all speakers across all speech tasks.
Nasalance values were established for adult and child JA speakers. The nasalance values were higher for Jordanian adults than for Jordanian children. No differences in nasalance were found between male and female Arabic speakers for nonnasal elements. JA speakers were significantly more nasal than SAE speakers on selected speech tasks.
Nasalance values for JA speakers were established for both adults and children, and Arabic speech materials were developed. The results of this investigation add to the growing body of research documenting differences in nasalance scores for normal speakers across different languages.

Although familiarity with a language impacts how phonology and semantics are processed at the neural level, little is known how these processes are affected by familiarity with a dialect. By measuring event-related potentials (ERPs) in kindergarten children we investigated neural processing related to familiarity with dialect-specific pronunciation and lexicality of spoken words before literacy acquisition in school. Children speaking one of two German dialects were presented with spoken word-picture pairings, in which congruity (or the lack thereof) was defined by dialect familiarity with pronunciation or vocabulary. In a dialect-independent control contrast, congruity was defined by audio-visual semantic (mis)match. Congruity effects and congruity-by-dialect group interactions in the ERPs were tested by data-driven topographic analyses of variance (TANOVA) and theory-driven focal analyses. Converging results revealed similar congruity effects in the N400 and late-positive-complex (LPC) in the control contrast for both dialect groups. In the dialect-specific vocabulary contrast, topographies of the N400- and LPC-effects were reversed depending on familiarity with the presented dialect words. In the dialect-specific pronunciation contrast, again a topography reversal was found depending on dialect familiarity, however, only for the LPC. Our data suggest that neural processing of unfamiliar words, but not pronunciation variants, is characterized by semantic processing (increased N400-effect). However, both unfamiliar words and pronunciation variants seem to engage congruity judgment, as indicated by the LPC-effect. Thus, semantic processing of pronunciation in dialect words seems to be rather robust against slight alterations in pronunciation, like changes in vowel duration, while such alterations may still trigger subsequent control processes.