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.
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.
arXiv:2607. 04154v1 Announce Type: cross Abstract: This paper explains the principles and provides examples of a new method for distinguishing between FAKE human speech synthesized by generative AI and natural speech.
By Yusei Tamura, Shigekazu Ishihara, Ken Ito
The paper introduces a method for measuring accent differences that balances interpretability and practicality. It proposes using articulatory representations obtained via articulatory inversion as an interpretable basis for accent comparison, while employing optimal transport to compare accents across any type of recording. This approach aims to overcome the limitations of traditional phonetic analyses and embedding‑based methods, which are either time‑consuming or non‑interpretable.
By Charles McGhee, Mark J. F. Gales, Kate M. Knill
The paper evaluates bias in phoneme-based automatic speech recognition (ASR) systems, focusing on WhisperIPA and ZIPA, which produce International Phonetic Alphabet (IPA) transcriptions. Using multilingual speech corpora and demographically annotated English datasets, the authors compare model-generated IPA against grapheme-to-phoneme (G2P) outputs with both standard phoneme error rate (PER) and a new Soft PER metric that allows linguistically similar substitutions. The study finds persistent disparities across language, gender, accent, ethnicity, and age, even when accounting for acceptable phonemic variation.
By Maneesha Rani Saha, Catherine Bao, Neal Patwari
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