Enhancing Cross-lingual Transfer via Phonemic Transcription Integration

Previous cross-lingual transfer methods are restricted to orthographic representation learning via textual scripts. This limitation hampers cross-lingual transfer and is biased towards languages sharing similar well-known scripts. To alleviate the gap between languages from different writing scripts, we propose PhoneXL, a framework incorporating phonemic transcriptions as an additional linguistic modality beyond the traditional orthographic transcriptions for cross-lingual transfer. Particularly, we propose unsupervised alignment objectives to capture (1) local one-to-one alignment between the two different modalities, (2) alignment via multi-modality contexts to leverage information from additional modalities, and (3) alignment via multilingual contexts where additional bilingual dictionaries are incorporated. We also release the first phonemic-orthographic alignment dataset on two token-level tasks (Named Entity Recognition and Part-of-Speech Tagging) among the understudied but interconnected Chinese-Japanese-Korean-Vietnamese (CJKV) languages. Our pilot study reveals phonemic transcription provides essential information beyond the orthography to enhance cross-lingual transfer and bridge the gap among CJKV languages, leading to consistent improvements on cross-lingual token-level tasks over orthographic-based multilingual PLMs.Comment: 11 pages,1 figure, 7 tables. To appear in Findings of ACL 202

Automatic Transcription of Northern Prinmi Oral Art: Approaches and Challenges to Automatic Speech Recognition for Language Documentation

One significant issue facing language documentation efforts is the transcription bottleneck: each documented recording must be transcribed and annotated, and these tasks are extremely labor intensive (Ćavar et al., 2016). Researchers have sought to accelerate these tasks with partial automation via forced alignment, natural language processing, and automatic speech recognition (ASR) (Neubig et al., 2020). Neural network—especially transformer-based—approaches have enabled large advances in ASR over the last decade. Models like XLSR-53 promise improved performance on under-resourced languages by leveraging massive data sets from many different languages (Conneau et al., 2020). This project extends these efforts to a novel context, applying XLSR-53 to Northern Prinmi, a Tibeto-Burman Qiangic language spoken in Southwest China (Daudey & Pincuo, 2020). Specifically, this thesis aims to answer two questions. First, is the XLSR-53 ASR model useful for first-pass transcription of oral art recordings from Northern Prinmi, an under-resourced tonal language? Second, does preprocessing target transcripts to combine grapheme clusters—multi-character representations of lexical tones and characters with modifying diacritics—into more phonologically salient units improve the model\u27s predictions? Results indicate that—with substantial adaptations—XLSR-53 will be useful for this task, and that preprocessing to combine grapheme clusters does improve model performance

A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments

Most speech and language technologies are trained with massive amounts of speech and text information. However, most of the world languages do not have such resources or stable orthography. Systems constructed under these almost zero resource conditions are not only promising for speech technology but also for computational language documentation. The goal of computational language documentation is to help field linguists to (semi-)automatically analyze and annotate audio recordings of endangered and unwritten languages. Example tasks are automatic phoneme discovery or lexicon discovery from the speech signal. This paper presents a speech corpus collected during a realistic language documentation process. It is made up of 5k speech utterances in Mboshi (Bantu C25) aligned to French text translations. Speech transcriptions are also made available: they correspond to a non-standard graphemic form close to the language phonology. We present how the data was collected, cleaned and processed and we illustrate its use through a zero-resource task: spoken term discovery. The dataset is made available to the community for reproducible computational language documentation experiments and their evaluation.Comment: accepted to LREC 201

Phonetic lessons from automatic phonemic transcription: preliminary reflections on Na (Sino-Tibetan) and Tsuut’ina (Dene) data

International audienceAutomatic phonemic transcription tools now reach high levels of accuracy on a single speaker with relatively small amounts of training data: on the order of 100 to 250 minutes of transcribed speech. Beyond its practical usefulness for language documentation, use of automatic transcription also yields some insights for phoneticians. The present report illustrates this by going into qualitative error analysis on two test cases, Yongning Na (Sino-Tibetan) and Tsuut’ina (Dene). Among other benefits, error analysis allows for a renewed exploration of phonetic detail: examining the output of phonemic transcription software compared with spectrographic and aural evidence. From a methodological point of view, the present report is intended as a case study in Computational Language Documentation: an interdisciplinary approach that associates fieldworkers (“diversity linguists”) and computer scientists with phoneticians/phonologists

Integrating Automatic Transcription into the Language Documentation Workflow: Experiments with Na Data and the Persephone Toolkit

Automatic speech recognition tools have potential for facilitating language documentation, but in practice these tools remain little-used by linguists for a variety of reasons, such as that the technology is still new (and evolving rapidly), user-friendly interfaces are still under development, and case studies demonstrating the practical usefulness of automatic recognition in a low-resource setting remain few. This article reports on a success story in integrating automatic transcription into the language documentation workflow, specifically for Yongning Na, a language of Southwest China. Using Persephone, an open-source toolkit, a single-speaker speech transcription tool was trained over five hours of manually transcribed speech. The experiments found that this method can achieve a remarkably low error rate (on the order of 17%), and that automatic transcriptions were useful as a canvas for the linguist. The present report is intended for linguists with little or no knowledge of speech processing. It aims to provide insights into (i) the way the tool operates and (ii) the process of collaborating with natural language processing specialists. Practical recommendations are offered on how to anticipate the requirements of this type of technology from the early stages of data collection in the field.National Foreign Language Resource Cente

Universal Automatic Phonetic Transcription into the International Phonetic Alphabet

This paper presents a state-of-the-art model for transcribing speech in any language into the International Phonetic Alphabet (IPA). Transcription of spoken languages into IPA is an essential yet time-consuming process in language documentation, and even partially automating this process has the potential to drastically speed up the documentation of endangered languages. Like the previous best speech-to-IPA model (Wav2Vec2Phoneme), our model is based on wav2vec 2.0 and is fine-tuned to predict IPA from audio input. We use training data from seven languages from CommonVoice 11.0, transcribed into IPA semi-automatically. Although this training dataset is much smaller than Wav2Vec2Phoneme's, its higher quality lets our model achieve comparable or better results. Furthermore, we show that the quality of our universal speech-to-IPA models is close to that of human annotators.Comment: 5 pages, 7 table

Blowing in the wind: using ‘North Wind and the Sun’ texts to sample phoneme inventories

Language documentation faces a persistent and pervasive problem: How much material is enough to represent a language fully? How much text would we need to sample the full phoneme inventory of a language? In the phonetic/phonemic domain, what proportion of the phoneme inventory can we expect to sample in a text of a given length? Answering these questions in a quantifiable way is tricky, but asking them is necessary. The cumulative collection of Illustrative Texts published in the Illustration series in this journal over more than four decades (mostly renditions of the ‘North Wind and the Sun’) gives us an ideal dataset for pursuing these questions. Here we investigate a tractable subset of the above questions, namely: What proportion of a language’s phoneme inventory do these texts enable us to recover, in the minimal sense of having at least one allophone of each phoneme? We find that, even with this low bar, only three languages (Modern Greek, Shipibo and the Treger dialect of Breton) attest all phonemes in these texts. Unsurprisingly, these languages sit at the low end of phoneme inventory sizes (respectively 23, 24 and 36 phonemes). We then estimate the rate at which phonemes are sampled in the Illustrative Texts and extrapolate to see how much text it might take to display a language’s full inventory. Finally, we discuss the implications of these findings for linguistics in its quest to represent the world’s phonetic diversity, and for JIPA in its design requirements for Illustrations and in particular whether supplementary panphonic texts should be included.1 Introduction 2 Language sample 3 Data coding 3.1 Lengh/gemination 3.2 Diphthongs vs. vowel sequences 3.3 Tonal contrasts 3.4 Illustration of an Illustration: Shilluk 4 Overview of the JIPA Illustration text corpus 5 Transcript coverage 6 The nature of phoneme frequency distributions 7 Recovering the full phoneme inventory 8 Evaluating the methods against a larger corpus 9 Returning to the cross-linguistic data 10 Estimating the amount of audio needed 11 Effect on recovery of cross-linguistic frequency/rarity 12 Discussion 12.1 Recommendations for JIPA 12.2 How many data are needed to fully capture a language’s phoneme inventory? 13 Conclusio

Prosodic description: An introduction for fieldworkers

This article provides an introductory tutorial on prosodic features such as tone and accent for researchers working on little-known languages. It specifically addresses the needs of non-specialists and thus does not presuppose knowledge of the phonetics and phonology of prosodic features. Instead, it intends to introduce the uninitiated reader to a field often shied away from because of its (in part real, but in part also just imagined) complexities. It consists of a concise overview of the basic phonetic phenomena (section 2) and the major categories and problems of their functional and phonological analysis (sections 3 and 4). Section 5 gives practical advice for documenting and analyzing prosodic features in the field.National Foreign Language Resource Cente