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Found 7 papers, 0 papers with code
Revisiting Pretraining with Adapters
Pretrained language models have served as the backbone for many state-of-the-art NLP results.
Accelerator-Aware Training for Transducer-Based Speech Recognition
Machine learning model weights and activations are represented in full-precision during training.
Sub-8-bit quantization for on-device speech recognition: a regularization-free approach
For on-device automatic speech recognition (ASR), quantization aware training (QAT) is ubiquitous to achieve the trade-off between model predictive performance and efficiency.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition
Very recently, as an alternative to LSTM layers, the Conformer architecture was introduced where the encoder of RNN-T is replaced with a modified Transformer encoder composed of convolutional layers at the frontend and between attention layers.
Sub-8-Bit Quantization Aware Training for 8-Bit Neural Network Accelerator with On-Device Speech Recognition
We present a novel sub-8-bit quantization-aware training (S8BQAT) scheme for 8-bit neural network accelerators.
A neural prosody encoder for end-ro-end dialogue act classification
Dialogue act classification (DAC) is a critical task for spoken language understanding in dialogue systems.
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding
Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio.
Natural Language Understanding
Spoken Language Understanding
