The study examines whether English homophones differ in phonetic realization beyond spoken word duration. Analyzing 14,000 homophone tokens from American television news, it finds that pairs such as "weight" and "wait" exhibit distinct phonetic patterns that can be predicted from their meanings in context. These differences persist even after controlling for duration, and time‑normalized spectrograms prove effective for detecting such fine‑grained phonetic variation without relying on phonetic transcriptions.
By Yu-Hsiang Tseng, Mirjam T. C. Ernestus, Louis F. M. ten Bosch, R. Harald Baayen
The study examines how English homophones—words that sound identical but have different meanings—are pronounced differently in spoken language. Analyzing 14,000 homophone tokens from American television news, researchers found that pairs such as "weight" and "wait" exhibit distinct phonetic realizations that can be predicted from their contextual meanings, even after controlling for word duration. Time‑normalized spectrograms proved effective for detecting these subtle differences without relying on phonetic transcriptions.
arXiv:2611.05803v1 Announce Type: new
Abstract: How much phonological knowledge do language models (LMs) possess about Chinese characters, and how is this knowledge represented and used? We introduce...
By Xiaozhe Ji, Renfen Hu, Hai Hu
Time-normalized f0 contours of Mandarin words in conversational speech have been shown to be predictable in part from their contextualized embeddings (CEs). The present study investigates whether CEs also predict spoken word duration for 7470 tokens of Mandarin monosyllabic CV words extracted from a Mandarin corpus of spontaneous speech.
The neutral, or floating, tone of Mandarin Chinese is a tone with an enigmatic set of properties. It has been described as a reduced tone, or as a tone that sometimes is lexically fixed but that can also be toneless.
arXiv:2610.04898v2 Announce Type: replace-cross
Abstract: This study analyzes the predictive power of LM-derived, token-level surprisal on Mandarin Chinese reading times. We first propose the Shortes...
By Hongao Zhu, Muxiaoqiao Xu, Yikang Liu, Siyuan Song, Yuxia Wang, Byung-Doh Oh, Hai Hu
The study introduces a scalable acoustic‑masking method to quantify how much each consonant contributes to word intelligibility. By silencing individual consonants in isolated words and measuring misrecognition rates with three ASR models, the authors define a mask‑induced misrecognition rate (MMR). Across English, Spanish, German, and Czech, MMR negatively correlates with phoneme frequency and positively with functional load, revealing that consonant importance varies by language.
By Eunjung Yeo, Kwanghee Choi, Krupaben Kothadia, Visar Berisha, Julie M. Liss, David R. Mortensen, David Harwath
arXiv:2606. 17835v1 Announce Type: cross Abstract: This study examines the extent to which the wav2vec2.
By James Kirby, Ioana Krehan, Michele Gubian
arXiv:2609. 21362v1 Announce Type: new Abstract: Conventional tokenizers represent text as characters or statistically derived subwords, overlooking the internal phonological structure of syllables and often requiring large vocabularies.
By Nghia Hieu Nguyen, Thai Bao Huynh, Binh-An Dinh-Le, Phu Gia Hoang, Dat Tien Nguyen, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
arXiv:2603.17306v4 Announce Type: replace
Abstract: Associations between speech sounds and meaning are well documented but have not been systematically mapped over a whole alphabet. Here we map them...
By Gexin Zhao
The study shows that previously reported gains in decoding spoken words from non‑invasive brain recordings can largely be replicated without any brain data, due to a timing shortcut where overlapping windows reveal word durations. By processing each window independently rather than jointly, the authors eliminate this shortcut, enabling the neural network to learn genuine brain‑based word information. This change improves performance and makes two existing strategies—aggregating predictions from multiple neural responses and incorporating a pretrained language model—significantly more effective, achieving a word error rate of 36.6% on a perceived speech benchmark.
By Dulhan Jayalath, Oiwi Parker Jones
arXiv:2608.22452v1 Announce Type: new
Abstract: Surprisal, the negative log-probability a language model assigns to a word given its preceding context, reliably predicts adult reading times. Does it...
By Francisco Portillo L\'opez