Výsledky vyhledávání - "Saito Daisuke"

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A Pilot Study of Applying Sequence-to-Sequence Voice Conversion to Evaluate the Intelligibility of L2 Speech Using a Native Speaker's Shadowings

Autor: Geng, Haopeng, Saito, Daisuke, Minematsu, Nobuaki

Utterances by L2 speakers can be unintelligible due to mispronunciation and improper prosody. In computer-aided language learning systems, textual feedback is often provided using a speech recognition engine. However, an ideal form of feedback for L2

Externí odkaz: http://arxiv.org/abs/2410.02239

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Simulating Native Speaker Shadowing for Nonnative Speech Assessment with Latent Speech Representations

Autor: Geng, Haopeng, Saito, Daisuke, Minematsu, Nobuaki

Evaluating speech intelligibility is a critical task in computer-aided language learning systems. Traditional methods often rely on word error rates (WER) provided by automatic speech recognition (ASR) as intelligibility scores. However, this approac

Externí odkaz: http://arxiv.org/abs/2409.11742

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A Pilot Study of GSLM-based Simulation of Foreign Accentuation Only Using Native Speech Corpora

Autor: Onda, Kentaro, Park, Joonyong, Minematsu, Nobuaki, Saito, Daisuke

We propose a method of simulating the human process of foreign accentuation using Generative Spoken Language Model (GSLM) only with native speech corpora. When one listens to spoken words of a foreign language and repeats them, the repeated speech is

Externí odkaz: http://arxiv.org/abs/2407.11370

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Do learned speech symbols follow Zipf's law?

Autor: Takamichi, Shinnosuke, Maeda, Hiroki, Park, Joonyong, Saito, Daisuke, Saruwatari, Hiroshi

In this study, we investigate whether speech symbols, learned through deep learning, follow Zipf's law, akin to natural language symbols. Zipf's law is an empirical law that delineates the frequency distribution of words, forming fundamentals for sta

Externí odkaz: http://arxiv.org/abs/2309.09690

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