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.
1, we use a 7 layer deep neural network with 2 convolutional layers, 4 dense layers and followed by an out- put layer
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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.