The Tonogenesis Continuum in Tibetan: A Computational Investigation

• URL: http://arxiv.org/abs/2510.22485v1
• Date: Sun, 26 Oct 2025 01:56:39 GMT
• Title: The Tonogenesis Continuum in Tibetan: A Computational Investigation
• Abstract summary: computational approach that quantifies functional role of pitch at different stages of sound change.<n>Atonal Amdo dialects tolerate pitch removal the most, while fully tonal U-Tsang varieties show severe degradation.<n>Findings suggest that traditional functional load metrics, based solely on minimal pairs, may overestimate pitch dependence in transitional systems.
• Score: 3.2199378493446904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tonogenesis-the historical process by which segmental contrasts evolve into lexical tone-has traditionally been studied through comparative reconstruction and acoustic phonetics. We introduce a computational approach that quantifies the functional role of pitch at different stages of this sound change by measuring how pitch manipulation affects automatic speech recognition (ASR) performance. Through analysis on the sensitivity to pitch-flattening from a set of closely related Tibetan languages, we find evidence of a tonogenesis continuum: atonal Amdo dialects tolerate pitch removal the most, while fully tonal U-Tsang varieties show severe degradation, and intermediate Kham dialects fall measurably between these extremes. These gradient effects demonstrate how ASR models implicitly learn the shifting functional load of pitch as languages transition from consonant-based to tone-based lexical contrasts. Our findings show that computational methods can capture fine-grained stages of sound change and suggest that traditional functional load metrics, based solely on minimal pairs, may overestimate pitch dependence in transitional systems where segmental and suprasegmental cues remain phonetically intertwined.

Related papers

• DeMTS: Denoising Trajectories as Multivariate Time Series for Hallucination Detection in Diffusion Language Models [59.414664850109155]
  Diffusion large language models (D-LLMs) have emerged as a promising paradigm for text generation.<n>D-LLMs remain vulnerable to hallucinations, where fluent outputs may contain factually incorrect or unsupported content.
  arXiv  Detail & Related papers  (2026-07-24T06:45:43Z)
• Synthesizing the Lombard Effect: Multi-Level Control of Speech Clarity and Vocal Effort in TTS [61.29502937013759]
  We introduce a flow-matching based text-to-speech model trained with vocal effort and articulation pseudo-labels.<n>The proposed model achieves continuous and disentangled control of vocal effort and articulation, while also enabling word-level emphasis for clarifying specific segments of an utterance.
  arXiv  Detail & Related papers  (2026-06-22T11:14:22Z)
• Assessing the Ability of Neural TTS Systems to Model Consonant-Induced F0 Perturbation [30.798688471243413]
  This study proposes a segmental-level prosodic probing framework to evaluate neural TTS models' ability to reproduce consonant-induced f0 perturbations.<n>We compare synthetic and natural speech realizations for thousands of words, stratified by lexical frequency, using Tacotron 2 and FastSpeech 2 trained on the same speech corpus (LJ Speech)<n>Results show accurate reproduction for high-frequency words but poor generalization to low-frequency items.
  arXiv  Detail & Related papers  (2026-03-22T06:06:47Z)
• Hallucination Benchmark for Speech Foundation Models [33.92968426403491]
  Hallucinations in automatic speech recognition (ASR) systems refer to fluent and coherent transcriptions produced by neural ASR models that are completely unrelated to the underlying acoustic input (i.e., the speech signal)<n>This apparent coherence can mislead subsequent processing stages and introduce serious risks, particularly in critical domains such as healthcare and law.<n>We introduce SHALLOW, the first benchmark framework that systematically categorizes and quantifies hallucination phenomena in ASR along four complementary axes: lexical, phonetic, morphological, and semantic.
  arXiv  Detail & Related papers  (2025-10-18T16:26:16Z)
• New Insights into Optimal Alignment of Acoustic and Linguistic Representations for Knowledge Transfer in ASR [30.00166986946003]
  We take a new insight to regard alignment and matching as a detection problem.<n>The goal is to identify meaningful correspondences with high precision and recall ensuring full coverage of linguistic tokens.<n>We propose an unbalanced optimal transport-based alignment model that explicitly handles distributional mismatch and structural asymmetries.
  arXiv  Detail & Related papers  (2025-09-06T05:58:52Z)
• Improved Intelligibility of Dysarthric Speech using Conditional Flow Matching [0.0]
  Dysarthria is a neurological disorder that significantly impairs speech intelligibility.<n>This necessitates the development of robust dysarthric-to-regular speech conversion techniques.
  arXiv  Detail & Related papers  (2025-06-19T08:24:17Z)
• Languages in Multilingual Speech Foundation Models Align Both Phonetically and Semantically [58.019484208091534]
  Cross-lingual alignment in pretrained language models (LMs) has enabled efficient transfer in text-based LMs.<n>It remains an open question whether findings and methods from text-based cross-lingual alignment apply to speech.
  arXiv  Detail & Related papers  (2025-05-26T07:21:20Z)
• DyPCL: Dynamic Phoneme-level Contrastive Learning for Dysarthric Speech Recognition [12.923409319624254]
  We propose a Dynamic Phoneme-level Contrastive Learning (DyPCL) method to bridge dysarthric speech recognition gaps.<n>We decompose the speech utterance into phoneme segments for phoneme-level contrastive learning, leveraging dynamic connectionist temporal classification alignment.<n>Our approach to training by difficulty levels alleviates the inherent variability of speakers, better identifying challenging speeches.
  arXiv  Detail & Related papers  (2025-01-31T10:25:42Z)
• Devising a Set of Compact and Explainable Spoken Language Feature for Screening Alzheimer's Disease [52.46922921214341]
  Alzheimer's disease (AD) has become one of the most significant health challenges in an aging society.<n>We devised an explainable and effective feature set that leverages the visual capabilities of a large language model (LLM) and the Term Frequency-Inverse Document Frequency (TF-IDF) model.<n>Our new features can be well explained and interpreted step by step which enhance the interpretability of automatic AD screening.
  arXiv  Detail & Related papers  (2024-11-28T05:23:22Z)
• Continuous Modeling of the Denoising Process for Speech Enhancement Based on Deep Learning [61.787485727134424]
  We use a state variable to indicate the denoising process. A UNet-like neural network learns to estimate every state variable sampled from the continuous denoising process. Experimental results indicate that preserving a small amount of noise in the clean target benefits speech enhancement.
  arXiv  Detail & Related papers  (2023-09-17T13:27:11Z)
• Latent Class-Conditional Noise Model [54.56899309997246]
  We introduce a Latent Class-Conditional Noise model (LCCN) to parameterize the noise transition under a Bayesian framework. We then deduce a dynamic label regression method for LCCN, whose Gibbs sampler allows us efficiently infer the latent true labels. Our approach safeguards the stable update of the noise transition, which avoids previous arbitrarily tuning from a mini-batch of samples.
  arXiv  Detail & Related papers  (2023-02-19T15:24:37Z)
• Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem [65.25725367771075]
  This study demonstrates, for the first time, that the synthesis-based approach can also perform well on this problem. Specifically, we propose a novel speech separation/enhancement model based on the recognition of discrete symbols. By utilizing the synthesis model with the input of discrete symbols, after the prediction of discrete symbol sequence, each target speech could be re-synthesized.
  arXiv  Detail & Related papers  (2021-12-17T08:35:40Z)
• A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer's Type [10.665308703417665]
  In recent years there has been a burgeoning interest in the use of computational methods to distinguish between elicited speech samples produced by patients with dementia, and those from healthy controls. The difference between perplexity estimates from two neural language models (LMs) has been shown to produce state-of-the-art performance. We find that perplexity of neural LMs is strongly and differentially associated with lexical frequency, and that a mixture model resulting from interpolating control and dementia LMs improves upon the current state-of-the-art for models trained on transcript text exclusively.
  arXiv  Detail & Related papers  (2020-05-07T16:22:48Z)
• Simultaneous Denoising and Dereverberation Using Deep Embedding Features [64.58693911070228]
  We propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features. At the denoising stage, the DC network is leveraged to extract noise-free deep embedding features. At the dereverberation stage, instead of using the unsupervised K-means clustering algorithm, another neural network is utilized to estimate the anechoic speech.
  arXiv  Detail & Related papers  (2020-04-06T06:34:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.