Papers with phone recognition
CTC-based Compression for Direct Speech Translation (2021.eacl-main)
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| Challenge: | Existing studies have shown that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST). |
| Approach: | They propose a method which performs a phone-informed compression of the input audio in direct ST models by exploiting the Connectionist Temporal Classification (CTC) they demonstrate that their method brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German) |
| Outcome: | The proposed method brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German) it reduces memory footprint by more than 10%, and is faster than previous approaches. |
Phone Inventories and Recognition for Every Language (2022.lrec-1)
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| Challenge: | Identifying phone inventories is crucial component in language documentation and preservation of endangered languages. |
| Approach: | They propose a probabilistic and non-probabilistic phone inventory model that estimates the phone inventory for any language listed in Glottolog. |
| Outcome: | The proposed model outperforms baseline models by 6.5 F1 and improves the PER (phone error rate) in phone recognition by 25%. |
POWSM: A Phonetic Open Whisper-Style Speech Foundation Model (2026.acl-long)
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Chin-Jou Li, Kalvin Chang, Shikhar Bharadwaj, Eunjung Yeo, Kwanghee Choi, Jian Zhu, David R. Mortensen, Shinji Watanabe
| Challenge: | Phone-level modeling of speech is a common approach to speech recognition, but it relies on task-specific architectures and datasets. |
| Approach: | They propose a phonetic framework capable of performing multiple phone-related tasks . they propose 'Phonetic Open Whisper-style Speech Model' that can perform these tasks together . |
| Outcome: | The proposed model outperforms or matches specialized PR models of similar size while supporting G2P, P2G, and ASR. |