Creating Digital Learning and Reference Resources for S outhern M ichif (2024)

Abstract: Minority and Indigenous languages are often under-documented and under-resourced. Where such resources do exist, particularly in the form of legacy materials, they are often inaccessible to learners and educators involved in revitalization efforts, whether due to the limitations of their original formats or the structure of their contents. Digitizing such resources and making them available on a variety of platforms is one step in overcoming these barriers. This is a major undertaking which requires significant expertise at the intersection of documentary linguistics, computational linguistics, and software development, and must be done while walking alongside speakers and language specialists in the community. We discuss the particular strategies and challenges involved in the development of one such resource, and make recommendations for future projects with a similar goal of mobilizing legacy language resources.

## introduction michif, ma-laañg-inaan, katawashishin 1 (heather souter). southern michif (iso 639-3: crg; hereafter "michif"), is one of three language varieties spoken by the métis @xcite . it is a contact language combining elements from algonquian languages-plains cree and the saulteaux dialect of ojibwe-with métis french. michif has traditionally been spoken in small, diasporic communities across western canada and the northern united states, mainly on the prairies. reliable census data regarding the current number of michif speakers are unavailable, largely due to ambiguity around the use of the label "michif", however, southern michif speakers and community members who are actively involved in communitybased language revitalization informally estimate that there are likely fewer than 100 speakers today 1 michif, our language, is beautiful. @xcite . intergenerational transmission of the language has ceased, and all but one or two mother-tongue speakers are over 70 years of age. despite growing revitalization activities in métis communities in western canada, few print and digital resources based on best practices in lexicography, language documentation, and second language acquisition are available to support those efforts. the primary aim of this project was to digitize and make accessible an out-of-print michif dictionary @xcite , while also developing local capacity in technologies for indigenous language documentation and revitalization. with the assistance of michif first-language speakers, community-based language workers, project partners, and computational linguists, we have developed the michif talking dictionary, foot_0 a digital spoken version of this important print resource. this dictionary is now available as a progressive web application, adapted to a wide variety of screen sizes, as shown in figure 1 . the application does not require an internet connection to search and browse once accessed. its source code, along with the code used to process the text and annotated speech data for the dictionary, is publicly available under an open-source license. 3 another major goal of this project was to develop capacity through the training of emerging métis community linguists, language workers, and scholars in the areas of audio recording, application of speech technologies, and annotation. between september 2019 and may 2021, one workshop on recording and five workshops on annotation were held in brandon, manitoba, ottawa, ontario, and online via zoom. 4 the original book, the michif dictionary: turtle mountain chippewa cree, is recognized for its valuable contribution to michif language documentation. however this now out-of-print resource is largely inaccessible to learners of michif unless purchased used at a high price, and is rarely available for purchase. while other michif dictionaries that include audio from native speakers have been published and made available in electronic format (e.g., @xcite , both of which are based primarily on michif as it is spoken in manitoba and saskatchewan), this dictionary is exceptional in its degree of coverage of lexical items and example sentences. in addition, many important linguistic studies of michif (e.g. @xcite , as well as the lexical resources mentioned above, have relied to varying degrees on the contents of the original turtle mountain dictionary as one of their primary sources. the turtle mountain dictionary is also an important historical resource, as many métis community members in canada have kinship ties to belcourt, nd, where the dictionary was created, and because it includes the speech of an under-represented dialect of michif. for all of these reasons, multiple elders and community members identified the creation of an electronic edition of this dictionary as a priority, as it is viewed as a resource that is much too valuable to remain inaccessible, but should rather be put into the hands of michif language learners and educators. permission was granted by turtle mountain community college, the dictionary's copyright holder, to the project team to create a digital version of the dictionary for online, offline, and mobile use. this "new" version retains all of the original content, but will also allow for the inclusion audio recordings of headwords and example sentences, as well as further enrichment in the eventual addition of alternate orthographies and grammatical information for lexical entries. ## recording for the dictionary, 181 hours of high-quality audio recordings were collected from four separate speakers. one speaker, verna demontigny, recorded the entire dictionary from cover to cover, while others recorded selected portions of it. thus, all entries have been recorded by at least one speaker, with some entries being recorded by two or more speakers. as shown in all the recordings, backed up regularly on multiple hard drives and on dropbox, were named according to a consistent file-naming process. metadata for each session, such as speaker name, location, and covered pages of the dictionary, was tracked and shared among team members via a google spreadsheet. as we will discuss below, the management of metadata was one of several challenges we faced in the production of the dictionary; for example, the information in this spreadsheet ultimately diverged from that contained in the annotation files. in our discussion of these challenges we hope to identify pitfalls and propose solutions for other groups involved in a similar endeavour. in this case, in the absence of a content management system for the recordings, this problem could have been partially mitigated with the "data validation" feature, similar to the use of controlled vocabularies in elan. ## annotation all audio recordings were annotated using elan @xcite to produce timealigned transcripts. first, each recording was segmented into pause-delimited utterances automatically using a deep neural network (dnn) voice activity detection service that was developed within the vesta-elan project by the centre de recherche informatique de montréal @xcite . this auto-segmentation saved an immeasurable amount of time in the annotation process. to support remote annotators with heterogeneous internet connections and computer hardware, hosting of the annotations was switched to google drive from dropbox. as per the requirements of earlier versions of elan, it was necessary to provide wav files to visualize waveforms, which were critical for annotators to be able to see and correct the automatic segmentation. however, dissemination of the 'master' wav files was a challenge, given their large size. to address this, we first down-sampled the original audio from 48 khz, 24-bit wav into two different formats: (1) high-quality mp3 files (44.1khz, 16-bit, 128kbps), which were used for playback; and (2) low-quality wav files (8khz, 8-bit), which were provided only for waveform visualization in elan, and were never used in playback. this approach made it feasible to share the entire audio collection with annotators over a cloud-based service, enabling them to both listen to high-quality versions of the audio and to display the corresponding waveforms in elan. the master recordings were maintained separately and later used as the source of the audio that was included in the dictionary. the paper dictionary was scanned and converted to text using the tesseract 4 optical character recognition engine. an elan template was created with tiers for english headwords, michif definitions, and example sentences, and these were then integrated from the ocr text of the dictionary into these tran-scripts by a team of indigenous and non-indigenous language workers who contributed to the project as paid contract employees, volunteers, and, in one case, as a student in a for-credit independent study course in applied linguistics. in most cases, the speakers recorded multiple instances of each word and example sentence. the annotators were therefore instructed to select the best recording for "export" to the talking dictionary. due to the slow and careful speaking style used, the example sentences and definitions were frequently split into multiple segments, which had to be reassembled in the construction of the talking dictionary. annotators were also instructed to adjust the boundaries of these segments to ensure that no words were cut off. in some cases, it was necessary to splice together different instances in order to obtain an audio clip without false starts or mispronunciations. because of the dialect variation which exists in southern michif, as well as the fact that the recordings were made nearly 40 years after the creation of the print dictionary, the speakers often diverge from the original text, or in some cases, provide a corrected version of a dictionary entry. annotators were thus instructed to flag partial matches as well as novel forms. in the initial version of the the talking dictionary, we have attempted to remain faithful to the original text as much as possible, with the exception of typos and misspellings. a revised version is in development which will present these variant and corrected forms along with relevant grammatical information. manual review and corrections of the text of the dictionary was performed by 14 undergraduate students as part of a community service-learning project in an indigenous languages of canada course in winter 2021. students in this course used transkribus lite, a web-based interface to functions of the transkribus transcription platform @xcite , to identify and address errors in the computer-readable text of the dictionary that were introduced by the previously applied ocr methods (e.g., correcting misspelled words, entering words or lines that were present on the page but missed by the ocr software, etc.). errors were found and corrected on a total of 1600 lines of text, or 8.5% of the dictionary. however, there remained a large number of systematic ocr errors, such as ambiguity between l, 1, and i, which were corrected semi-automatically in the dictionary build. since different parts of the project were conducted simultaneously, technical issues arose from the ordering of this work. for instance, the postcorrection of the dictionary text took place after the start of the annotation process, resulting in a divergence between the text in the elan annotations and the text dictionary. likewise, while the dictionary entries from the original ocr output were separated into definitions and examples when creating the elan files, and sometimes also corrected by the annotators afterwards, these modifications were not synchronized or linked in any way to the dictionary text. because it was infeasible to correct these discrepancies manually, it was necessary to develop a complex data extraction process using heuristic matching algorithms to align dictionary text and annotations. ## dictionary construction the electronic dictionary was produced using a customized version of the mothertongues @xcite platform. this well-documented opensource tool provides web and mobile applications with a flexible and configurable approximate search feature, shown in figure 2 , along with a tool to automate the conversion of dictionaries from a variety of formats including spreadsheets, xml, and json files. compared to tools such as flex @xcite , it supports a very restricted set of lexicographical data, but no such data exists in the original dictionary in any case. this is a common situation for community-developed resources, and the relatively lightweight nature of mothertongues allows for the creation of dictionaries with a minimum of technical expertise. that said, the absence of grammatical information in the michif talking dictionary limits its usefulness for language learners, and we hope to address this in a subsequent revision. as detailed in sections 2 and 3, there were four separate sources of information used to produce the talking dictionary: 1. the corrected ocr dictionary text. 2. the original recordings. 3. the metadata spreadsheet identifying the speaker, date and location of each recording along with the pages of the dictionary covered and any comments on audio quality. 4. the elan files containing speech segments and aligned lexical entries and examples for each recording. unfortunately, the need to rapidly organize a distributed annotation effort, turnover of key personnel, and other difficulties arising from the covid-19 pandemic led to widespread inconsistencies within and between these data sources. the initial version of the talking dictionary reflected these inconsistencies; the audio was widely misattributed, mismatched with the text, and of poor quality as it was mistakenly taken from the low-quality files used for visualization rather than the original master recordings. in the absence of a content management system adapted to this task, it is imperative that the project manager work in close collaboration with technical resources to identify and correct these problems. it would be useful to continuously build and deploy the electronic version of the dictionary, and to track any integration problems, from the beginning of the annotation process. the first priority when building the dictionary was thus the reconstruction of the metadata and retrieval of the original audio files. as well, while the post-correction of the ocr output resulted in a fairly consistently formatted text faithful to the original print version, the organization of the entries in this text created numerous problems when converting them to a structured format for presentation. among other things, this required the development of a language identification system, detailed in section 4.1. finally, after extracting structured text from the dictionary entries, a subsequent matching was performed against the elan annotations to identify and extract the corresponding audio segments. because of the divergence between original and postcorrected text, as well as the fact that annotators frequently (but inconsistently) corrected the text in the annotations, this required a multi-stage heuristic strategy in order to maximise the audio coverage, detailed in section 4.2. because of the extensive recording and annotation efforts detailed in section 2, there are often recordings of multiple speakers for both michif definitions and example sentences. this level of complexity in the dictionary entries was not supported by the current version of mothertongues at the time. we therefore extended both the dictionary builder and the web user interface to support it, using a more flexible json-based input format. 5 in order to quantify progress in improving the conversion workflow, 100 random entries were sampled and manually converted to this format, and the performance of the system evaluated using precision and recall over definitions and examples. along with the audio coverage, this f1 score was also recorded and tracked for each weekly build of the dictionary during the development process. ## verification and re-annotation in testing the talking dictionary, it became obvious that many audio entries were incomplete or mismatched to the text. given the scale of the recordings and annotations and the limited resources available, we attempted to use force-alignment to detect these problems, similarly to how the festvox system @xcite excludes incorrectly labeled prompts to avoid egregious errors in unit-selection synthesis. of course, no pretrained acoustic models exist for michif. using the "universal" grapheme-to-phoneme technique from @xcite , we create an approximate phonetic transcription of the michif text, then use the same alignment technique as @xcite with a narrow beam search, flagging examples that fail to align for review. to streamline the workflow, we collect the audio clips on an html page, shown in figure 5 , which we package with the relevant elan annotation files and preference (.pfsx) files which direct elan to open directly on the annotation to be reviewed. since false positives are not problematic (we can simply listen to them to determine that they are correct), a weak alignment model of this sort is quite effective, allowing us to detect and correct several hundred annotations which could not be fixed by the automated processes described in section 4.2, generally in cases where one segment of an example that was split by vad was not properly labeled by the annotators. an unintended side benefit of this verification is that it gives us word-level time alignments for the example sentences. we therefore extended the mothertongues system to include a "read-along" style highlighting of each word when listening to the examples in the talking dictionary, as shown in figure 6 . ## conclusions some of the technical difficulties we had to overcome in creating this resource stem from organizational difficulties exacerbated by the covid-19 pandemic. others may simply be inherent to a large-scale, widely distributed and heterogeneous data collection and annotation effort. for future projects of this scale, it is crucial to endure that metadata is continuously validated and to avoid, at all costs, duplicating it across multiple unsynchronized data sources. it is equally important to involve a variety of perspectives in the design of the data collection and processing workflows, including members of the speech community, documentary and computational linguists, and to allow for iterative improvements to these processes. when structured data is created as part of the data collection and annotation process, this data should be considered authoritative and maintained as such. if created from an unstructured data source (such as the ocr output of the paper dictionary), there should either be a robust process to pull changes and corrections from this original data source into the structured data, or the original unstructured data should be archived and left alone. this may require careful consideration of the dependencies between different steps in the process to avoid duplicate or conflicting efforts. some of these issues could be avoided with sufficient and appropriate tooling. in particular, while elan is a robust and highly useful tool for annotation, it is difficult to integrate with external sources of metadata, distributed filesystems, or version control systems. while elan is highly extensible, with numerous third-party plug-ins and add-ons, it inherently operates at a single-file level, making it cumbersome to perform tasks involving individual annotations across a large number of eaf files. this could potentially be achieved by adding an api to elan which would allow it to be controlled by an external content management system. overall, this project has resulted in a resource that will be of long-term use in michif language teaching, revitalization, and study. the dictionary application is now not only accessible to a wide range of users, but is also searchable, and the recorded michif pronunciations of the headwords and example sentences will be extremely valuable for learners. moreover, a total of 16 métis team members were trained in language documentation and indigenous language technologies, developing local capacity. in particular, the annotators involved in this project developed technical skills while also gaining valuable exposure to the michif language. they will be able to carry this experience and knowledge with them as they continue their language journeys and contribute to future language revitalization initiatives.