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Found 20 papers, 0 papers with code
Search Optimization with Query Likelihood Boosting and Two-Level Approximate Search for Edge Devices
We present a novel search optimization solution for approximate nearest neighbor (ANN) search on resource-constrained edge devices.
Accelerator-Aware Training for Transducer-Based Speech Recognition
Machine learning model weights and activations are represented in full-precision during training.
Robust Acoustic and Semantic Contextual Biasing in Neural Transducers for Speech Recognition
Prior approaches typically relied on subword encoders for encoding the bias phrases.
Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers
Streaming speech recognition architectures are employed for low-latency, real-time applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
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.
Dialog act guided contextual adapter for personalized speech recognition
Specifically, it leverages dialog acts to select the most relevant user catalogs and creates queries based on both -- the audio as well as the semantic relationship between the carrier phrase and user catalogs to better guide the contextual biasing.
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)
+3
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.
Compute Cost Amortized Transformer for Streaming ASR
We present a streaming, Transformer-based end-to-end automatic speech recognition (ASR) architecture which achieves efficient neural inference through compute cost amortization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Latency Control for Keyword Spotting
We also show that when our approach is used in conjunction with a max-pooling loss, we are able to improve relative false accepts by 25 % at a fixed latency when compared to cross entropy loss.
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
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.
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding
In addition, the NLU model in the two-stage system is not streamable, as it must wait for the audio segments to complete processing, which ultimately impacts the latency of the SLU system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
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
Bifocal Neural ASR: Exploiting Keyword Spotting for Inference Optimization
We present Bifocal RNN-T, a new variant of the Recurrent Neural Network Transducer (RNN-T) architecture designed for improved inference time latency on speech recognition tasks.
Learning a Neural Diff for Speech Models
As more speech processing applications execute locally on edge devices, a set of resource constraints must be considered.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Amortized Neural Networks for Low-Latency Speech Recognition
We apply AmNets to the Recurrent Neural Network Transducer (RNN-T) to reduce compute cost and latency for an automatic speech recognition (ASR) task.
Ranked #55 on
Speech Recognition
on LibriSpeech test-clean
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
CoDERT: Distilling Encoder Representations with Co-learning for Transducer-based Speech Recognition
We find that tandem training of teacher and student encoders with an inplace encoder distillation outperforms the use of a pre-trained and static teacher transducer.
Semantic Complexity in End-to-End Spoken Language Understanding
We perform experiments where we vary the semantic complexity of a large, proprietary dataset and show that STI model performance correlates with our semantic complexity measures, such that performance increases as complexity values decrease.
Statistical Model Compression for Small-Footprint Natural Language Understanding
In this paper we investigate statistical model compression applied to natural language understanding (NLU) models.
