A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition

By Hao Tang, Wei-Ning Hsu, François Grondin, James R. Glass. 2018

26 citations recorded by the source corpus.

Speech recognizers trained on close-talking speech do not generalize to distant speech and the word error rate degradation can be as large as 40% absolute. Most studies focus on tackling distant speech recognition as a separate problem, leaving little effort to adapting close-talking speech recognizers to distant speech. In this work, we review several approaches from a domain adaptation perspective. These approaches, including speech enhancement, multi-condition training, data augmentation, and autoencoders, all involve a transformation of the data between domains. We conduct experiments on the AMI data set, where these approaches can be realized under the same controlled setting. These approaches lead to different amounts of improvement under their respective assumptions. The purpose of this paper is to quantify and characterize the performance gap between the two domains, setting up the basis for studying adaptation of speech recognizers from close-talking speech to distant speech. Our results also have implications for improving distant speech recognition.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training (2021, neutral): “This includes data augmentation [13, 14], self-training on target domain [15], domain adversarial training [16], and joint training [8].”
On Training Recurrent Networks with Truncated Backpropagation Through time in Speech Recognition (2018, methodology): “In addition, for the baseline we use a 7-layer time-delay neural network (TDNN) with 512 hidden units in each layer and an architecture described in [26, 6].”