Multi-user VoiceFilter-Lite via Attentive Speaker Embedding

Closing the Gap between Single-User and Multi-User VoiceFilter-Lite

VoiceFilter-Lite is a speaker-conditioned voice separation model that plays a crucial role in improving speech recognition and speaker verification by suppressing overlapping speech from non-target speakers. However, one limitation of VoiceFilter-Lite, and other speaker-conditioned speech models in general, is that these models are usually limited to a single target speaker. This is undesirable as most smart home devices now support multiple enrolled users. In order to extend the benefits of personalization to multiple users, we previously developed an attention-based speaker selection mechani…

VoiceFilter-Lite: Streaming Targeted Voice Separation for On-Device Speech Recognition

We introduce VoiceFilter-Lite, a single-channel source separation model that runs on the device to preserve only the speech signals from a target user, as part of a streaming speech recognition system. Delivering such a model presents numerous challenges: It should improve the performance when the input signal consists of overlapped speech, and must not hurt the speech recognition performance under all other acoustic conditions. Besides, this model must be tiny, fast, and perform inference in a streaming fashion, in order to have minimal impact on CPU, memory, battery and latency. We propose n…

Abstractive Text Summarization using Attentive GRU based Encoder-Decoder

In todays era huge volume of information exists everywhere. Therefore, it is very crucial to evaluate that information and extract useful, and often summarized, information out of it so that it may be used for relevant purposes. This extraction can be achieved through a crucial technique of artificial intelligence, namely, machine learning. Indeed automatic text summarization has emerged as an important application of machine learning in text processing. In this paper, an english text summarizer has been built with GRU-based encoder and decoder. Bahdanau attention mechanism has been added to o…

Question Answering over Freebase via Attentive RNN with Similarity Matrix based CNN

With the rapid growth of knowledge bases (KBs), question answering over knowledge base, a.k.a. KBQA has drawn huge attention in recent years. Most of the existing KBQA methods follow so called encoder-compare framework. They map the question and the KB facts to a common embedding space, in which the similarity between the question vector and the fact vectors can be conveniently computed. This, however, inevitably loses original words interaction information. To preserve more original information, we propose an attentive recurrent neural network with similarity matrix based convolutional neural…

从实例级自注意力 Hawkes 过程中学习 Granger 因果关系

Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes

提出 ISAHP 神经点过程框架，利用 transformer 自注意力直接从多类型异步事件序列中无监督推断实例级 Granger 因果关系，并在因果发现与类型预测任务上达到最优性能。

混合多头注意力Unet-3D用于脑肿瘤分割

Hybrid Multihead Attentive Unet-3D for Brain Tumor Segmentation

本文提出一种混合多头注意力U-Net架构，用于提高脑肿瘤分割的准确性，在BraTS 2020数据集上优于SegNet、FCN-8s和Dense121 U-Net。