87 citations · 127 across the 8 of their papers we have counts for
8 papers
Internalizing Curriculum Judgment for LLM Reinforcement Fine-Tuning
Han Zheng, Yining Ma, Karthick Gunasekaran +4
In LLM Reinforcement Fine-Tuning (RFT), curriculum learning drives both efficiency and performance. Yet, current methods externalize curriculum judgment via handcrafted heuristics…
Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets
Lu Zeng, Sree Hari Krishnan Parthasarathi, Yuzong Liu +4
We propose a novel 2-stage sub 8-bit quantization aware training algorithm for all components of a 250K parameter feedforward, streaming, state-free keyword spotting model. For the…
On Front-end Gain Invariant Modeling for Wake Word Spotting
Yixin Gao, Noah D. Stein, Chieh-Chi Kao +4
Wake word (WW) spotting is challenging in far-field due to the complexities and variations in acoustic conditions and the environmental interference in signal transmission. A suite…
Towards Data-efficient Modeling for Wake Word Spotting
Yixin Gao, Yuriy Mishchenko, Anish Shah +2
Wake word (WW) spotting is challenging in far-field not only because of the interference in signal transmission but also the complexity in acoustic environments. Traditional WW mod…
Accurate Detection of Wake Word Start and End Using a CNN
Christin Jose, Yuriy Mishchenko, Thibaud Senechal +3
Small footprint embedded devices require keyword spotters (KWS) with small model size and detection latency for enabling voice assistants. Such a keyword is often referred to as \t…
Sub-band Convolutional Neural Networks for Small-footprint Spoken Term Classification
Chieh-Chi Kao, Ming Sun, Yixin Gao +2
This paper proposes a Sub-band Convolutional Neural Network for spoken term classification. Convolutional neural networks (CNNs) have proven to be very effective in acoustic applic…