Small Footprint Convolutional Recurrent Networks for Streaming Wakeword Detection

Tiny-CRNN: Streaming Wakeword Detection In A Low Footprint Setting

In this work, we propose Tiny-CRNN (Tiny Convolutional Recurrent Neural Network) models applied to the problem of wakeword detection, and augment them with scaled dot product attention. We find that, compared to Convolutional Neural Network models, False Accepts in a 250k parameter budget can be reduced by 25% with a 10% reduction in parameter size by using models based on the Tiny-CRNN architecture, and we can get up to 32% reduction in False Accepts at a 50k parameter budget with 75% reduction in parameter size compared to word-level Dense Neural Network models. We discuss solutions to the c…

RepCNN：微型但强大的唤醒词检测模型

RepCNN: Micro-sized, Mighty Models for Wakeword Detection

RepCNN 通过多分支训练、单分支推理的重新参数化方法，在保持极低内存和计算占用的同时，显著提升了常开唤醒词检测模型的准确率与运行效率。

Stacked 1D convolutional networks for end-to-end small footprint voice trigger detection

We propose a stacked 1D convolutional neural network (S1DCNN) for end-to-end small footprint voice trigger detection in a streaming scenario. Voice trigger detection is an important speech application, with which users can activate their devices by simply saying a keyword or phrase. Due to privacy and latency reasons, a voice trigger detection system should run on an always-on processor on device. Therefore, having small memory and compute cost is crucial for a voice trigger detection system. Recently, singular value decomposition filters (SVDFs) has been used for end-to-end voice trigger dete…

HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words

Streaming keyword spotting is a widely used solution for activating voice assistants. Deep Neural Networks with Hidden Markov Model (DNN-HMM) based methods have proven to be efficient and widely adopted in this space, primarily because of the ability to detect and identify the start and end of the wake-up word at low compute cost. However, such hybrid systems suffer from loss metric mismatch when the DNN and HMM are trained independently. Sequence discriminative training cannot fully mitigate the loss-metric mismatch due to the inherent Markovian style of the operation. We propose an low footp…

ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition

The recurrent neural network transducer (RNN-T) is a prominent streaming end-to-end (E2E) ASR technology. In RNN-T, the acoustic encoder commonly consists of stacks of LSTMs. 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. In this paper, we introduce a new streaming ASR model, Convolutional Augmented Recurrent Neural Network Transducers (ConvRNN-T) in which we augment the LSTM-based RNN-T with a nove…

一种面向移动全双工语音交互的小占用声学回声消除方案

A Small-footprint Acoustic Echo Cancellation Solution for Mobile Full-Duplex Speech Interactions

本文提出一种基于神经网络的移动全双工语音交互小占用声学回声消除方案，结合数据增强、渐进式学习及定制后处理策略，显著提升了语音质量及VAD和ASR等下游任务的效果。