Search Results for author:
Found 8 papers, 0 papers with code
Streaming Anchor Loss: Augmenting Supervision with Temporal Significance
Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms.
Does Single-channel Speech Enhancement Improve Keyword Spotting Accuracy? A Case Study
Our investigation reveals that SE can improve KWS accuracy on noisy speech when the backend model is trained on clean speech; however, despite our extensive exploration, it is difficult to improve the KWS accuracy with SE when the backend is trained on noisy speech.
Leveraging Large Language Models for Exploiting ASR Uncertainty
While large language models excel in a variety of natural language processing (NLP) tasks, to perform well on spoken language understanding (SLU) tasks, they must either rely on off-the-shelf automatic speech recognition (ASR) systems for transcription, or be equipped with an in-built speech modality.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models
We also show that the ensemble of the LatticeRNN and acoustic-distilled models brings further accuracy improvement of 20%.
Streaming on-device detection of device directed speech from voice and touch-based invocation
When interacting with smart devices such as mobile phones or wearables, the user typically invokes a virtual assistant (VA) by saying a keyword or by pressing a button on the device.
Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigation
We propose a streaming transformer (TF) encoder architecture, which progressively processes incoming audio chunks and maintains audio context to perform both VTD and FTM tasks using only acoustic features.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Progressive Voice Trigger Detection: Accuracy vs Latency
We first demonstrate that by including more audio context after a detected trigger phrase, we can indeed get a more accurate decision.
Hybrid Transformer/CTC Networks for Hardware Efficient Voice Triggering
Our baseline is an acoustic model(AM), with BiLSTM layers, trained by minimizing the CTC loss.
