Leveraging Deep Learning to Shed Light on Tones of an Endangered Language: A Case Study of M oklen (2024)

Abstract: Moklen, a tonal Austronesian language spoken in Thailand, exhibits two tones with unbalanced distributions. We employed machine learning techniques for time-series classification to investigate its acoustic properties. Our analysis reveals that a synergy between pitch and vowel quality is crucial for tone distinction, as the model trained with these features achieved the highest accuracy.

## introduction moklen, an endangered and understudied austronesian language spoken along the western coast of southern thailand @xcite , has sparked debate about its tonal status. while austronesian languages are typically not tonal, moklen exhibits a few minimal pairs suggesting the presence of two lexical tones @xcite . the acoustic properties of moklen tone were recently explored by @xcite . their study revealed that moklen tones are distinguished not only by pitch, but also by vowel quality and voice quality. interestingly, these acoustic characteristics resemble those of register contrasts found in mainland southeast asian languages @xcite . it is worth noting that the definition of tone in this paper refers to the suprasegmental contrast, which may be realized not only by pitch, but also by voice quality or vowel quality, similar to vietnamese, burmese, shanghai chinese, and other languages (see @xcite . what remains unclear is the relative weight of acoustic cues in moklen tones and register systems. phonetic contrasts often differ across multiple dimensions; for example, the english /b/ and /p/ differ in their voice onset time (vot) as well as other dimensions, including the duration of stop closure and fundamental frequency (f0) after closure @xcite . furthermore, even though a contrast may involve several phonetic dimensions, they may not all be equally important. in other words, the phonetic cues may have different weights in production and/or perception. for instance, the english /b/ and /p/ are primarily distinguished by vot, with f0 playing a secondary role @xcite . it is therefore possible that the acoustic cues in moklen tones, including pitch, vowel quality, and voice quality, may have different relative weights. in this paper, we investigate the contribution of individual acoustic features to moklen tone distinction using an ablation study within a machine learning framework. we employed a bidirectional long short-term memory (bilstm) neural network with self-attention for sequence classification. bilstm with self-attention has been used in tone recognition tasks in previous works (e.g., @xcite . however, neural network classification has rarely been used with the tones of underrepresented languages such as moklen. this investigation confirms the presence of contrastive tones in moklen. furthermore, our analysis reveals that pitch and vowel quality features are crucial for distinguishing the two lexical tones. the model trained on this feature set achieved the highest accuracy in differentiating between moklen tones. ## discussion and conclusion our investigation into moklen tone classification using acoustic features sheds light on the nature of tones in this unique austronesian language. the ablation study confirmed that all features (pitch, voice quality, and vowel quality) contribute to moklen tone classification. this is evidenced by the findings that models utilizing only a single feature set representing each acoustic aspect achieved relatively good performance (> 70% accuracy). however, the model combining pitch and vowel quality achieved the highest overall accuracy and f1-score. this result suggests that a synergy between pitch and vowel quality information plays a crucial role in distinguishing the two moklen tones. one potential explanation for the importance of pitch and vowel quality in distinguishing moklen tones lies in their historical development. as mentioned, tonal contrast in moklen is an innovation absent in its ancestral language. moklen tones may have developed from reanalyzing different contrasts, such as stress, that utilize pitch and vowel quality @xcite furthermore, we observed that the models excluding the pitch features achieved the lowest accuracy. this finding confirms that pitch emerges as the primary cue for moklen tone. on the other hand, although other acoustic cues can be used to distinguish the two moklen tones, they appear to play a more secondary role. our analysis of the features' relative importance across time steps within the vowel interval revealed that the most important features cluster around the vowel onset. this suggests that the distinction between tone 1 and tone 2 is most salient at the onset of the vowel. this pattern closely aligns with register contrast, where the distinction between registers is most prominent at the vowel onset @xcite . we also conducted an error analysis on the bestperforming model, examining four features: onset voicing, vowel length, coda manners, and coda places of articulation. chi-squared tests revealed that vowel length and coda places of articulation significantly impacted the model's classification, with words having long vowels and velar codas being more frequently misclassified. further investigation is needed to understand why vowel length and coda place of articulation affected the model's performance. one potential aspect for future work is to investigate moklen tones from the perspective of acoustic features within a larger time interval, such as the entire syllable rather than just the vowel interval used in this paper. in other words, there may be more aspects of the tones that we have not yet explored. this broader analysis could include features like the f0 peak location on the final open syllable or final syllable with sonorant coda, as shown in figure 1 , where tone 1 generally exhibits an earlier peak compared to tone 2. further investigation into the perception of the two tones by moklen speakers could provide deeper insights into the nature of this unique tonal system. this study demonstrates the potential of machine learning approaches for analyzing acoustic features in endangered languages like moklen. by leveraging deep learning for tone classification, we can gain valuable insights into the sound system of a language, even with limited documentation or speaker availability. one limitation of moklen tone documentation is that tones are not predictable from the phonological environment or comparative studies, making it challenging for language fieldworkers to identify tones in moklen words. classification models trained on words with identified tones can assist fieldworkers in identifying the tones of undocumented words. furthermore, these models can aid in creating a dictionary of moklen, which is an important step in language revitalization.