Tone in Perspective: A Computational Typological Analysis of Tone Function in ASR (2025)

Abstract: This study investigates the impact of pitch flattening on automatic speech recognition (ASR) performance across tonal and non-tonal languages. Using vocoder-based signal processing techniques, we created pitch-flattened versions of speech recordings and compared ASR performance against original recordings. Results reveal that tonal languages experience substantially larger performance degradation than non-tonal languages. Analysis of tone confusion matrices shows systematic patterns of misidentification where contour tones collapse toward level tones when pitch information is removed. Calculation of tone’s functional load at syllable and word levels demonstrates that syllable-level functional load strongly predicts ASR vulnerability to pitch flattening, while word-level patterns reflect each language’s morphological structure. These findings illuminate the differential importance of pitch information across languages and suggest that ASR systems for languages with high syllable-level functional load require more robust pitch modeling.

## introduction lexical tone, where pitch distinctions signal differences in word meaning, is a core feature of over half the world's languages @xcite . while tonal contrasts rely primarily on fundamental frequency (f 0 ), they also interact with duration, intensity, and voice quality. these complexities pose unique challenges for automatic speech recognition (asr), particularly in tonal languages where pitch plays a central role in lexical identity. recent asr models implicitly encode tonal information, but it remains unclear how critical pitch actually is for recognition across language types. to investigate this, we apply pitch flattening-a signal processing technique that removes f 0 contours-to speech recordings and compare asr performance with and without flattened pitch contours across both tonal (thai, vietnamese, mandarin) and nontonal (uzbek, indonesian, turkish) languages. we find that tonal languages experience significantly larger degradation in asr performance under pitch flattening, with systematic tone confusion patterns revealing that contour tones (e.g., falling, rising) tend to collapse toward level tones when f 0 contours are removed. to explain these differences, we compute the functional load of tone and show that syllable-level functional load is a strong predictor of asr vulnerability, capturing cross-linguistic differences in tone dependency more effectively than word-level metrics. ## methods we designed experiments to evaluate how pitch manipulation influences asr performance across typologically diverse languages. specifically, we investigate how removing lexical pitch cues via pitch flattening affects recognition accuracy in tonal versus non-tonal languages. by comparing asr performance on original and pitch-flattened versions of the same utterances, we aim to quantify the importance of pitch information for recognition and identify the linguistic and structural factors that predict vulnerability to pitch manipulation. ## discussion our results reveal significant differences in how the asr results of tonal and non-tonal languages respond to pitch flattening, with systematic patterns that illuminate the relationship between tone, speech perception, and asr performance. these findings have important implications for both lin-guistic theory and speech technology development. ## conclusion this study investigated the impact of pitch flattening on asr performance across tonal and non-tonal languages, revealing several key insights about the role of pitch in speech recognition. our findings demonstrate that tonal languages experience substantially greater performance degradation when pitch information is removed, but with significant variations that correlate with the functional load of tone in each language. the systematic patterns of tone confusion observed-where contour tones collapse toward level tones-highlight fundamental aspects of tonal perceptual structure. beyond documenting these effects, we established a quantitative relationship between information-theoretic measures of tone importance and asr vulnerability. languages with high syllable-level functional load proved most susceptible to pitch flattening, while word-level functional load patterns reflected each language's morphological characteristics. this framework offers a principled approach for predicting which languages will require more sophisticated tone modeling in speech technology applications. our findings have implications for both linguistic theory and asr system development. theoretically, they support models of tone perception where unmarked level tones serve as default categories when distinctive pitch information is unavailable. practically, they suggest that asr systems for tonal languages should be designed with language-specific considerations of tone's functional load and the availability of redundant acoustic cues. future work could extend this analysis to a wider typological range of tone systems. for instance, examining cantonese, which features a more complex inventory of level tones, could test whether our observed pattern of confusion toward level tones holds in languages where multiple level tones must be distinguished. similarly, investigating bantu languages, which feature tonal contrasts that are often analyzed differently from east asian systems despite having contour properties, would broaden our typological understanding of how different tone systems respond to pitch degradation.