Tootfinder

Pronunciation Deviation Analysis Through Voice Cloning and Acoustic Comparison
Andrew Valdivia, Yueming Zhang, Hailu Xu, Amir Ghasemkhani, Xin Qin
https://arxiv.org/abs/2507.10985

Pronunciation Deviation Analysis Through Voice Cloning and Acoustic Comparison
This paper presents a novel approach for detecting mispronunciations by analyzing deviations between a user's original speech and their voice-cloned counterpart with corrected pronunciation. We hypothesize that regions with maximal acoustic deviation between the original and cloned utterances indicate potential mispronunciations. Our method leverages recent advances in voice cloning to generate a synthetic version of the user's voice with proper pronunciation, then performs frame-by-frame compa…

V(is)owel: An Interactive Vowel Chart to Understand What Makes Visual Pronunciation Effective in Second Language Learning
Charlotte Kiesel, Dipayan Mukherjee, Mark Hasegawa-Johnson, Karrie Karahalios
https://arxiv.org/abs/2507.06202

V(is)owel: An Interactive Vowel Chart to Understand What Makes Visual Pronunciation Effective in Second Language Learning
Visual feedback speeds up learners' improvement of pronunciation in a second language. The visual combined with audio allows speakers to see sounds and differences in pronunciation that they are unable to hear. Prior studies have tested different visual methods for improving pronunciation, however, we do not have conclusive understanding of what aspects of the visualizations contributed to improvements. Based on previous work, we created V(is)owel, an interactive vowel chart. Vowel charts provi…

Evaluating Logit-Based GOP Scores for Mispronunciation Detection
Aditya Kamlesh Parikh, Cristian Tejedor-Garcia, Catia Cucchiarini, Helmer Strik
https://arxiv.org/abs/2506.12067

Evaluating Logit-Based GOP Scores for Mispronunciation Detection
Pronunciation assessment relies on goodness of pronunciation (GOP) scores, traditionally derived from softmax-based posterior probabilities. However, posterior probabilities may suffer from overconfidence and poor phoneme separation, limiting their effectiveness. This study compares logit-based GOP scores with probability-based GOP scores for mispronunciation detection. We conducted our experiment on two L2 English speech datasets spoken by Dutch and Mandarin speakers, assessing classification …

Pronunciation-Lexicon Free Training for Phoneme-based Crosslingual ASR via Joint Stochastic Approximation
Saierdaer Yusuyin, Te Ma, Hao Huang, Zhijian Ou
https://arxiv.org/abs/2507.06249

Pronunciation-Lexicon Free Training for Phoneme-based Crosslingual ASR via Joint Stochastic Approximation
Recently, pre-trained models with phonetic supervision have demonstrated their advantages for crosslingual speech recognition in data efficiency and information sharing across languages. However, a limitation is that a pronunciation lexicon is needed for such phoneme-based crosslingual speech recognition. In this study, we aim to eliminate the need for pronunciation lexicons and propose a latent variable model based method, with phonemes being treated as discrete latent variables. The new metho…

@… prepared for complaints about the proper pronunciation of "shout"

#English #spelling still manages to surprise me all the time. Especially with choosing “c” or “s” or “z”¹ in places for the basically same pronunciation (similarly with “ou” vs “u” (or “o”) and “e” vs “i”).
Trying to correct the spelling regularly gets me more confused ;-)
__
¹ok…

Intelligibility of Text-to-Speech Systems for Mathematical Expressions
Sujoy Roychowdhury, H. G. Ranjani, Sumit Soman, Nishtha Paul, Subhadip Bandyopadhyay, Siddhanth Iyengar
https://arxiv.org/abs/2506.11086

Intelligibility of Text-to-Speech Systems for Mathematical Expressions
There has been limited evaluation of advanced Text-to-Speech (TTS) models with Mathematical eXpressions (MX) as inputs. In this work, we design experiments to evaluate quality and intelligibility of five TTS models through listening and transcribing tests for various categories of MX. We use two Large Language Models (LLMs) to generate English pronunciation from LaTeX MX as TTS models cannot process LaTeX directly. We use Mean Opinion Score from user ratings and quantify intelligibility through…

Enhancing GOP in CTC-Based Mispronunciation Detection with Phonological Knowledge
Aditya Kamlesh Parikh, Cristian Tejedor-Garcia, Catia Cucchiarini, Helmer Strik
https://arxiv.org/abs/2506.02080

Enhancing GOP in CTC-Based Mispronunciation Detection with Phonological Knowledge
Computer-Assisted Pronunciation Training (CAPT) systems employ automatic measures of pronunciation quality, such as the goodness of pronunciation (GOP) metric. GOP relies on forced alignments, which are prone to labeling and segmentation errors due to acoustic variability. While alignment-free methods address these challenges, they are computationally expensive and scale poorly with phoneme sequence length and inventory size. To enhance efficiency, we introduce a substitution-aware alignment-fr…

Dhvani: A Weakly-supervised Phonemic Error Detection and Personalized Feedback System for Hindi
Arnav Rustagi, Satvik Bajpai, Nimrat Kaur, Siddharth Siddharth
https://arxiv.org/abs/2506.02166

Dhvani: A Weakly-supervised Phonemic Error Detection and Personalized Feedback System for Hindi
Computer-Assisted Pronunciation Training (CAPT) has been extensively studied for English. However, there remains a critical gap in its application to Indian languages with a base of 1.5 billion speakers. Pronunciation tools tailored to Indian languages are strikingly lacking despite the fact that millions learn them every year. With over 600 million speakers and being the fourth most-spoken language worldwide, improving Hindi pronunciation is a vital first step toward addressing this gap. This …

Pronunciation Editing for Finnish Speech using Phonetic Posteriorgrams
Zirui Li, Lauri Juvela, Mikko Kurimo
https://arxiv.org/abs/2507.02115 https://

Pronunciation Editing for Finnish Speech using Phonetic Posteriorgrams
Synthesizing second-language (L2) speech is potentially highly valued for L2 language learning experience and feedback. However, due to the lack of L2 speech synthesis datasets, it is difficult to synthesize L2 speech for low-resourced languages. In this paper, we provide a practical solution for editing native speech to approximate L2 speech and present PPG2Speech, a diffusion-based multispeaker Phonetic-Posteriorgrams-to-Speech model that is capable of editing a single phoneme without text al…