1 code implementation • 26 Jan 2022 • Piotr Żelasko, Siyuan Feng, Laureano Moro Velazquez, Ali Abavisani, Saurabhchand Bhati, Odette Scharenborg, Mark Hasegawa-Johnson, Najim Dehak
In this paper, we 1) investigate the influence of different factors (i. e., model architecture, phonotactic model, type of speech representation) on phone recognition in an unknown language; 2) provide an analysis of which phones transfer well across languages and which do not in order to understand the limitations of and areas for further improvement for automatic phone inventory creation; and 3) present different methods to build a phone inventory of an unseen language in an unsupervised way.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
1 code implementation • 22 Oct 2020 • Siyuan Feng, Piotr Żelasko, Laureano Moro-Velázquez, Ali Abavisani, Mark Hasegawa-Johnson, Odette Scharenborg, Najim Dehak
Furthermore, we find that a multilingual LM hurts a multilingual ASR system's performance, and retaining only the target language's phonotactic data in LM training is preferable.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 12 May 2020 • Ali Abavisani, Mark Hasegawa-Johnson
In this article, we provide a model to estimate a real-valued measure of the intelligibility of individual speech segments.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 9 Aug 2019 • Ali Abavisani, Mark A. Hasegawa-Johnson
We studied the effects of frequency fine-tuning of the primary cue by presenting tokens of the same consonant but different vowels with similar SNR90.