Towards a Phonology-Informed Evaluation of Multilingual TTS

• URL: http://arxiv.org/abs/2607.01965v1
• Date: Thu, 02 Jul 2026 09:57:54 GMT
• Title: Towards a Phonology-Informed Evaluation of Multilingual TTS
• Abstract summary: Neural TTS systems can sound natural across languages, but naturalness does not guarantee the preservation of sound contrasts that distinguish words from their grammatical forms.<n>We propose a classifier-based framework that audits TTS output against language-specific phonological patterns using human speech as a benchmark.
• Score: 3.0071070245633127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural TTS systems can sound natural across languages, but naturalness does not guarantee the preservation of sound contrasts that distinguish words from their grammatical forms. Standard metrics like MOS do not test for this. We propose a classifier-based framework that audits TTS output against language-specific phonological patterns using human speech as a benchmark. Testing Assamese advanced tongue root (ATR) vowel harmony with Meta's MMS TTS, we show that a classifier trained on human speech transfers to synthesized speech with minimal loss. The faithfulness audit reveals that [+ATR] mid vowels are realized as [-ATR] in 1/3 tokens despite an underlying [+ATR] specification, a bias absent in human speech. At the word level, predicted ATR labels classify harmony more accurately than transcription labels, indicating a gap between intended and produced phonology. The framework offers task-specific diagnostics and generalizes to other phonological contrasts with measurable acoustic cues.

Related papers

• Beyond Naturalness: Probing Automated Text-To-Speech Evaluators on Linguistically Grounded Dimensions [3.2559302660048597]
  Text-to-Speech (TTS) evaluation methods are expected to reflect human perception.<n>It is unclear how well they capture the distinct aspects of speech that listeners actually perceive.<n>We construct the first-level meta-evaluation benchmark for TTS, comprising 860 utterances annotated by trained linguist raters.
  arXiv  Detail & Related papers  (2026-08-10T17:59:51Z)
• Domain-Specific Evaluation of Text-to-Speech Systems: A Multi-Metric Benchmarking Study [1.224954637705144]
  This paper presents a reproducible, multi-metric benchmarking framework for systematic evaluation of modern TTS systems.<n>Four state-of-the-art TTS systems -- Indic-Parler-TTS, MMS-TTS, Microsoft Edge TTS, and Google Gemini TTS -- are evaluated.<n>Results reveal substantial variation in TTS performance across speech domains, with emotional speech consistently presenting the greatest synthesis challenge.
  arXiv  Detail & Related papers  (2026-08-03T13:49:11Z)
• Quantifying Speaker Embedding Phonological Rule Interactions in Accented Speech Synthesis [44.55147169458465]
  We analyze the interaction between speaker embeddings and linguistically motivated phonological rules in accented speech synthesis.<n>Experiments show that combining rules with embeddings yields more authentic accents.<n>Our findings highlight rules as a lever for accent control and a framework for evaluating disentanglement in speech generation.
  arXiv  Detail & Related papers  (2026-01-20T19:25:33Z)
• Towards Inclusive Communication: A Unified Framework for Generating Spoken Language from Sign, Lip, and Audio [52.859261069569165]
  We propose the first unified framework capable of handling diverse combinations of sign language, lip movements, and audio for spoken-language text generation.<n>We focus on three main objectives: (i) designing a unified, modality-agnostic architecture capable of effectively processing heterogeneous inputs; (ii) exploring the underexamined synergy among modalities, particularly the role of lip movements as non-manual cues in sign language comprehension; and (iii) achieving performance on par with or better than state-of-the-art models specialized for individual tasks.
  arXiv  Detail & Related papers  (2025-08-28T06:51:42Z)
• 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)
• An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios [76.11409260727459]
  This paper explores the language adaptation capability of ZMM-TTS, a recent SSL-based multilingual TTS system. We demonstrate that the similarity in phonetics between the pre-training and target languages, as well as the language category, affects the target language's adaptation performance.
  arXiv  Detail & Related papers  (2024-06-13T08:16:52Z)
• A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech [94.64927912924087]
  We train TTS systems using real-world speech from YouTube and podcasts. Recent Text-to-Speech architecture is designed for multiple code generation and monotonic alignment. We show thatRecent Text-to-Speech architecture outperforms existing TTS systems in several objective and subjective measures.
  arXiv  Detail & Related papers  (2023-02-08T17:34:32Z)
• Prosody-controllable spontaneous TTS with neural HMMs [11.472325158964646]
  We propose a TTS architecture that can rapidly learn to speak from small and irregular datasets. We add utterance-level prosody control to an existing neural HMM-based TTS system. We evaluate the system's capability of synthesizing two types of creaky voice.
  arXiv  Detail & Related papers  (2022-11-24T11:06:11Z)
• Cross-lingual Low Resource Speaker Adaptation Using Phonological Features [2.8080708404213373]
  We train a language-agnostic multispeaker model conditioned on a set of phonologically derived features common across different languages. With as few as 32 and 8 utterances of target speaker data, we obtain high speaker similarity scores and naturalness comparable to the corresponding literature.
  arXiv  Detail & Related papers  (2021-11-17T12:33:42Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)

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.