A bandwidth embedding, a small learned vector marking speech as narrowband or wideband, improves narrowband word error rate by 13% relative in a single mixed-bandwidth acoustic model without degrading wideband speech.
Further more, we also used different convolutional layers (re- ferred to as parallel convolutional layers) to handle the mis- match between the narrow and wideband speech
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.AS 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Bandwidth Embeddings for Mixed-bandwidth Speech Recognition
A bandwidth embedding, a small learned vector marking speech as narrowband or wideband, improves narrowband word error rate by 13% relative in a single mixed-bandwidth acoustic model without degrading wideband speech.