No abstract

No abstract

Mixup is a recently proposed technique that creates virtual training examples by combining existing ones. It has been successfully used in various machine learning tasks. This paper focuses on applying mixup to automatic speech recognition (ASR). More specifically, several strategies for acoustic model training are investigated, including both conventional cross-entropy and novel lattice-free MMI models. Considering mixup as a method of data augmentation as well as regularization, we compare it with widely used speed perturbation and dropout techniques. Experiments on Switchboard-1, AMI and TED-LIUM datasets shows consistent improvement of word error rate up to 13% relative. Moreover, mixup is found to be particularly effective on test data mismatched to the training data.

No abstract

This paper describes the Speech Technology Center (STC) system for the 5th CHiME challenge. This challenge considers the problem of distant multi-microphone conversational speech recognition in everyday home environments. Our efforts were focused on the single-array track, however, we participated in the multiple-array track as well. The system is in the ranking A of the challenge: acoustic models remain frame-level tied phonetic targets, lexicon and language model are not changed compared to the conventional ASR baseline. Our system employs a combination of 4 acoustic models based on convolutional and recurrent neural networks. Speaker adaptation with target speaker masks and multi-channel speaker-aware acoustic model with neural network beamforming are two major features of the system. Moreover, various techniques for improving acoustic models are applied, including array synchronization, data cleanup, alignment transfer, mixup, speed perturbation data augmentation, room simulation, and backstitch training. Our system scored 3rd in the single-array track with Word Error Rate (WER) of 55.5% and 4th in the multiple-array track with WER of 55.6% on the evaluation data, achieving a substantial improvement over the baseline system.