Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoring
L2 speech assessment has traditionally focused on phonetic assessment, leaving the scoring of suprasegmental features such as rhythm and intonation underexplored. Moreover, assessment methods often require training with labeled L2 speech data, making them difficult to apply in low-resource settings. We investigate whether DTW over self-supervised WavLM representations can provide a text-free framework for assessing phonetic accuracy, rhythm, and intonation in English and Japanese L2 speech. Results show that a basic DTW-based approach that compares learner speech to native templates exceeds hu
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- PossiblePossibly related (embedding) · 50%Introducing Real World VoiceEQ: Measuring the human quality of voice AI →
- FuzzySimilar title/name (fuzzy) · 87%huggingface/speech-to-speech →
“Fuzzy title match (0.94): “Self-supervised Speech Comparison for L2 Phone, Rhythm, and ” ≈ “huggingface/speech-to-speech””
- LinkedLinked via arxiv author · 85%Stephen McIntosh →
“Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoring”
- LinkedLinked via arxiv author · 85%Reuben Smit →
“Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoring”
- LinkedLinked via arxiv author · 85%Daisuke Saito →
“Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoring”
- LinkedLinked via arxiv author · 85%Nobuaki Minematsu →
“Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoring”
- LinkedLinked via arxiv author · 85%Herman Kamper →
“Self-supervised Speech Comparison for L2 Phone, Rhythm, and Intonation Scoring”