Search Results for author:
Found 11 papers, 1 papers with code
CoMERA: Computing- and Memory-Efficient Training via Rank-Adaptive Tensor Optimization
Our method also shows $\sim 2\times$ speedup than standard pre-training on a BERT-like code-generation LLM while achieving $4. 23\times$ compression ratio in pre-training.
Quantization-Aware and Tensor-Compressed Training of Transformers for Natural Language Understanding
To improve the convergence, a layer-by-layer distillation is applied to distill a quantized and tensor-compressed student model from a pre-trained transformer.
Dual-Attention Neural Transducers for Efficient Wake Word Spotting in Speech Recognition
We present dual-attention neural biasing, an architecture designed to boost Wake Words (WW) recognition and improve inference time latency on speech recognition tasks.
Contextual Adapters for Personalized Speech Recognition in Neural Transducers
Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Context-Aware Transformer Transducer for Speech Recognition
We also leverage both BLSTM and pretrained BERT based models to encode contextual data and guide the network training.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
FANS: Fusing ASR and NLU for on-device SLU
In this paper, we introduce FANS, a new end-to-end SLU model that fuses an ASR audio encoder to a multi-task NLU decoder to infer the intent, slot tags, and slot values directly from a given input audio, obviating the need for transcription.
Ranked #14 on
Spoken Language Understanding
on Fluent Speech Commands
(using extra training data)
Exploiting Large-scale Teacher-Student Training for On-device Acoustic Models
We present results from Alexa speech teams on semi-supervised learning (SSL) of acoustic models (AM) with experiments spanning over 3000 hours of GPU time, making our study one of the largest of its kind.
End-to-End Multi-Channel Transformer for Speech Recognition
Transformers are powerful neural architectures that allow integrating different modalities using attention mechanisms.
Tie Your Embeddings Down: Cross-Modal Latent Spaces for End-to-end Spoken Language Understanding
In this paper, we treat an E2E system as a multi-modal model, with audio and text functioning as its two modalities, and use a cross-modal latent space (CMLS) architecture, where a shared latent space is learned between the `acoustic' and `text' embeddings.
End-to-End Neural Transformer Based Spoken Language Understanding
In this paper, we introduce an end-to-end neural transformer-based SLU model that can predict the variable-length domain, intent, and slots vectors embedded in an audio signal with no intermediate token prediction architecture.
Streaming End-to-End Bilingual ASR Systems with Joint Language Identification
Experiments show that for English-Spanish, the bilingual joint ASR-LID architecture matches monolingual ASR and acoustic-only LID accuracies.
