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Title:Learning speech embeddings for speaker adaptation and speech understanding
Author(s):Sari, Leda
Director of Research:Hasegawa-Johnson, Mark
Doctoral Committee Chair(s):Hasegawa-Johnson, Mark
Doctoral Committee Member(s):Smaragdis, Paris; Do, Minh; Ji, Heng
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Neural networks
automatic speech recognition
speaker adaptation
spoken language understanding
fairness in machine learning
Abstract:In recent years, deep neural network models gained popularity as a modeling approach for many speech processing tasks including automatic speech recognition (ASR) and spoken language understanding (SLU). In this dissertation, there are two main goals. The first goal is to propose modeling approaches in order to learn speaker embeddings for speaker adaptation or to learn semantic speech embeddings. The second goal is to introduce training objectives that achieve fairness for the ASR and SLU problems. In the case of speaker adaptation, we introduce an auxiliary network to an ASR model and learn to simultaneously detect speaker changes and adapt to the speaker in an unsupervised way. We show that this joint model leads to lower error rates as compared to a two-step approach where the signal is segmented into single speaker regions and then fed into an adaptation model. We then reformulate the speaker adaptation problem from a counterfactual fairness point-of-view and introduce objective functions to match the ASR performance of the individuals in the dataset to that of their counterfactual counterparts. We show that we can achieve lower error rate in an ASR system while reducing the performance disparity between protected groups. In the second half of the dissertation, we focus on SLU and tackle two problems associated with SLU datasets. The first SLU problem is the lack of large speech corpora. To handle this issue, we propose to use available non-parallel text data so that we can leverage the information in text to guide learning of the speech embeddings. We show that this technique increases the intent classification accuracy as compared to a speech-only system. The second SLU problem is the label imbalance problem in the datasets, which is also related to fairness since a model trained on skewed data usually leads to biased results. To achieve fair SLU, we propose to maximize the F-measure instead of conventional cross-entropy minimization and show that it is possible to increase the number of classes with nonzero recall. In the last two chapters, we provide additional discussions on the impact of these projects from both technical and social perspectives, propose directions for future research and summarize the findings.
Issue Date:2021-04-05
Rights Information:Copyright 2021 Leda Sari
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05

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