4 papers · 1 filter
Benchmarking Prosody Encoding in Discrete Speech Tokens
Kentaro Onda, Satoru Fukayama, Daisuke Saito +1
Recently, discrete tokens derived from self-supervised learning (SSL) models via k-means clustering have been actively studied as pseudo-text in speech language models and as effic…
Differentiable K-means for Fully-optimized Discrete Token-based ASR
Kentaro Onda, Yosuke Kashiwagi, Emiru Tsunoo +2
Recent studies have highlighted the potential of discrete tokens derived from self-supervised learning (SSL) models for various speech-related tasks. These tokens serve not only as…
Prosodically Enhanced Foreign Accent Simulation by Discrete Token-based Resynthesis Only with Native Speech Corpora
Kentaro Onda, Keisuke Imoto, Satoru Fukayama +2
Recently, a method for synthesizing foreign-accented speech only with native speech data using discrete tokens obtained from self-supervised learning (SSL) models was proposed. Con…
Discrete Tokens Exhibit Interlanguage Speech Intelligibility Benefit: an Analytical Study Towards Accent-robust ASR Only with Native Speech Data
Kentaro Onda, Keisuke Imoto, Satoru Fukayama +2
In this study, we gained insight that contributes to achieving accent-robust ASR using only native speech data. In human perception of non-native speech, the phenomenon known as "i…