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17 papers
How Should We Evaluate Naturalness for Synthetic Speech with a Foreign Accent?
Kentaro Onda, Satoru Fukayama, Daisuke Saito +1
Recent advances in speech synthesis technology have enabled the generation of speech with diverse characteristics. One such characteristic is a foreign accent. In our preliminary e…
Subphonetic Acoustic Modeling via Optimal Transport for Pronunciation Assessment
Haopeng Geng, Jiun-Ting Li, Daisuke Saito +1
Pronunciation assessment requires acoustic evidence that is temporally precise, diagnostically meaningful, and faithful to the learner's actual production. However, existing acoust…
Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoring
Stephen McIntosh, Reuben Smit, Daisuke Saito +2
L2 speech assessment has traditionally focused on phonetic assessment, leaving the scoring of suprasegmental features such as rhythm and intonation underexplored. Moreover, assessm…
Phone Segmentation and Recognition through Phonological Activation Mapping
Shikhar Bharadwaj, Kwanghee Choi, Stephen McIntosh +8
Phone segmentation and recognition are inherently related tasks, yet modern approaches typically model them separately. We argue that phonetic structure is already latent in the re…
Speech Playground: An Interactive Tool for Speech Analysis and Comparison
Stephen McIntosh, Daisuke Saito, Nobuaki Minematsu
This paper presents Speech Playground, an interactive speech visualization and comparison tool. While existing tools such as Praat are excellent, it can be cumbersome to integrate…
Beyond Acoustic Sparsity and Linguistic Bias: A Prompt-Free Paradigm for Mispronunciation Detection and Diagnosis
Haopeng Geng, Longfei Yang, Xi Chen +3
Mispronunciation Detection and Diagnosis (MDD) requires modeling fine-grained acoustic deviations. However, current ASR-derived MDD systems often face inherent limitations. In part…
Prosodic ABX: A Language-Agnostic Method for Measuring Prosodic Contrast in Speech Representations
Haitong Sun, Stephen McIntosh, Kwanghee Choi +3
Speech representations from self-supervised speech models (S3Ms) are known to be sensitive to phonemic contrasts, but their sensitivity to prosodic contrasts has not been directly…
Kanade: A Simple Disentangled Tokenizer for Spoken Language Modeling
Zhijie Huang, Stephen McIntosh, Daisuke Saito +1
A good language model starts with a good tokenizer. Tokenization is especially important for speech modeling, which must handle continuous signals that mix linguistic and non-lingu…
Advanced Modeling of Interlanguage Speech Intelligibility Benefit with L1-L2 Multi-Task Learning Using Differentiable K-Means for Accent-Robust Discrete Token-Based ASR
Kentaro Onda, Satoru Fukayama, Daisuke Saito +1
Building ASR systems robust to foreign-accented speech is an important challenge in today's globalized world. A prior study explored the way to enhance the performance of phonetic…
MixedG2P-T5: G2P-free Speech Synthesis for Mixed-script texts using Speech Self-Supervised Learning and Language Model
Joonyong Park, Daisuke Saito, Nobuaki Minematsu
This study presents a novel approach to voice synthesis that can substitute the traditional grapheme-to-phoneme (G2P) conversion by using a deep learning-based model that generates…
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…
A Perception-Based L2 Speech Intelligibility Indicator: Leveraging a Rater's Shadowing and Sequence-to-sequence Voice Conversion
Haopeng Geng, Daisuke Saito, Nobuaki Minematsu
Evaluating L2 speech intelligibility is crucial for effective computer-assisted language learning (CALL). Conventional ASR-based methods often focus on native-likeness, which may f…
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…
Analytic Study of Text-Free Speech Synthesis for Raw Audio using a Self-Supervised Learning Model
Joonyong Park, Daisuke Saito, Nobuaki Minematsu
We examine the text-free speech representations of raw audio obtained from a self-supervised learning (SSL) model by analyzing the synthesized speech using the SSL representations…
A Pilot Study of GSLM-based Simulation of Foreign Accentuation Only Using Native Speech Corpora
Kentaro Onda, Joonyong Park, Nobuaki Minematsu +1
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 spoke…
Do learned speech symbols follow Zipf's law?
Shinnosuke Takamichi, Hiroki Maeda, Joonyong Park +2
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 deline…