Locality enhanced dynamic biasing and sampling strategies for contextual ASR
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929221236948992 |
|---|---|
| author | Jalal, Md Asif Parada, Pablo Peso Pavlidis, George Moschopoulos, Vasileios Saravanan, Karthikeyan Kontoulis, Chrysovalantis-Giorgos Zhang, Jisi Drosou, Anastasios Lee, Gil Ho Lee, Jungin Jung, Seokyeong |
| author_facet | Jalal, Md Asif Parada, Pablo Peso Pavlidis, George Moschopoulos, Vasileios Saravanan, Karthikeyan Kontoulis, Chrysovalantis-Giorgos Zhang, Jisi Drosou, Anastasios Lee, Gil Ho Lee, Jungin Jung, Seokyeong |
| contents | Automatic Speech Recognition (ASR) still face challenges when recognizing time-variant rare-phrases. Contextual biasing (CB) modules bias ASR model towards such contextually-relevant phrases. During training, a list of biasing phrases are selected from a large pool of phrases following a sampling strategy. In this work we firstly analyse different sampling strategies to provide insights into the training of CB for ASR with correlation plots between the bias embeddings among various training stages. Secondly, we introduce a neighbourhood attention (NA) that localizes self attention (SA) to the nearest neighbouring frames to further refine the CB output. The results show that this proposed approach provides on average a 25.84% relative WER improvement on LibriSpeech sets and rare-word evaluation compared to the baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_13146 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Locality enhanced dynamic biasing and sampling strategies for contextual ASR Jalal, Md Asif Parada, Pablo Peso Pavlidis, George Moschopoulos, Vasileios Saravanan, Karthikeyan Kontoulis, Chrysovalantis-Giorgos Zhang, Jisi Drosou, Anastasios Lee, Gil Ho Lee, Jungin Jung, Seokyeong Audio and Speech Processing Computation and Language Sound Automatic Speech Recognition (ASR) still face challenges when recognizing time-variant rare-phrases. Contextual biasing (CB) modules bias ASR model towards such contextually-relevant phrases. During training, a list of biasing phrases are selected from a large pool of phrases following a sampling strategy. In this work we firstly analyse different sampling strategies to provide insights into the training of CB for ASR with correlation plots between the bias embeddings among various training stages. Secondly, we introduce a neighbourhood attention (NA) that localizes self attention (SA) to the nearest neighbouring frames to further refine the CB output. The results show that this proposed approach provides on average a 25.84% relative WER improvement on LibriSpeech sets and rare-word evaluation compared to the baseline. |
| title | Locality enhanced dynamic biasing and sampling strategies for contextual ASR |
| topic | Audio and Speech Processing Computation and Language Sound |
| url | https://arxiv.org/abs/2401.13146 |