Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals

06/25/2020
by   Jing Shi, et al.
0

Neural sequence-to-sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus on one-to-many sequence transduction problems, such as extracting multiple sequential sources from a mixture sequence. We extend the standard sequence-to-sequence model to a conditional multi-sequence model, which explicitly models the relevance between multiple output sequences with the probabilistic chain rule. Based on this extension, our model can conditionally infer output sequences one-by-one by making use of both input and previously-estimated contextual output sequences. This model additionally has a simple and efficient stop criterion for the end of the transduction, making it able to infer the variable number of output sequences. We take speech data as a primary test field to evaluate our methods since the observed speech data is often composed of multiple sources due to the nature of the superposition principle of sound waves. Experiments on several different tasks including speech separation and multi-speaker speech recognition show that our conditional multi-sequence models lead to consistent improvements over the conventional non-conditional models.

READ FULL TEXT
research
10/15/2019

MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition

Recently, the end-to-end approach has proven its efficacy in monaural mu...
research
11/21/2019

Improving Conditioning in Context-Aware Sequence to Sequence Models

Neural sequence to sequence models are well established for applications...
research
11/19/2015

Order Matters: Sequence to sequence for sets

Sequences have become first class citizens in supervised learning thanks...
research
05/15/2018

A Purely End-to-end System for Multi-speaker Speech Recognition

Recently, there has been growing interest in multi-speaker speech recogn...
research
06/16/2021

Multi-Speaker ASR Combining Non-Autoregressive Conformer CTC and Conditional Speaker Chain

Non-autoregressive (NAR) models have achieved a large inference computat...
research
10/25/2018

Tackling Sequence to Sequence Mapping Problems with Neural Networks

In Natural Language Processing (NLP), it is important to detect the rela...
research
06/06/2018

Convolutional Sequence to Sequence Non-intrusive Load Monitoring

A convolutional sequence to sequence non-intrusive load monitoring model...

Please sign up or login with your details

Forgot password? Click here to reset