Phonetically-Augmented Discriminative Rescoring for Voice Search Error Correction
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912417374535680 |
|---|---|
| author | Van Gysel, Christophe Wu, Maggie Verwimp, Lyan Tirkaz, Caglar Bertola, Marco Lei, Zhihong Oualil, Youssef |
| author_facet | Van Gysel, Christophe Wu, Maggie Verwimp, Lyan Tirkaz, Caglar Bertola, Marco Lei, Zhihong Oualil, Youssef |
| contents | End-to-end (E2E) Automatic Speech Recognition (ASR) models are trained using paired audio-text samples that are expensive to obtain, since high-quality ground-truth data requires human annotators. Voice search applications, such as digital media players, leverage ASR to allow users to search by voice as opposed to an on-screen keyboard. However, recent or infrequent movie titles may not be sufficiently represented in the E2E ASR system's training data, and hence, may suffer poor recognition.
In this paper, we propose a phonetic correction system that consists of (a) a phonetic search based on the ASR model's output that generates phonetic alternatives that may not be considered by the E2E system, and (b) a rescorer component that combines the ASR model recognition and the phonetic alternatives, and select a final system output.
We find that our approach improves word error rate between 4.4 and 7.6% relative on benchmarks of popular movie titles over a series of competitive baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06117 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Phonetically-Augmented Discriminative Rescoring for Voice Search Error Correction Van Gysel, Christophe Wu, Maggie Verwimp, Lyan Tirkaz, Caglar Bertola, Marco Lei, Zhihong Oualil, Youssef Computation and Language Artificial Intelligence Information Retrieval End-to-end (E2E) Automatic Speech Recognition (ASR) models are trained using paired audio-text samples that are expensive to obtain, since high-quality ground-truth data requires human annotators. Voice search applications, such as digital media players, leverage ASR to allow users to search by voice as opposed to an on-screen keyboard. However, recent or infrequent movie titles may not be sufficiently represented in the E2E ASR system's training data, and hence, may suffer poor recognition. In this paper, we propose a phonetic correction system that consists of (a) a phonetic search based on the ASR model's output that generates phonetic alternatives that may not be considered by the E2E system, and (b) a rescorer component that combines the ASR model recognition and the phonetic alternatives, and select a final system output. We find that our approach improves word error rate between 4.4 and 7.6% relative on benchmarks of popular movie titles over a series of competitive baselines. |
| title | Phonetically-Augmented Discriminative Rescoring for Voice Search Error Correction |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2506.06117 |