Embedding models that encode semantic information into low-dimensional vector representations are useful in various machine learning tasks with limited training data. However, these models are typically too large to support inference in small edge devices, which motivates training of smaller yet comparably predictive student embedding models through knowledge distillation (KD). While knowledge distillation traditionally uses the teacher’s original training dataset to train the student, we hypothesize that using a dataset similar to the student’s target domain allows for better compression and training efficiency for the said domain, at the cost of reduced generality across other (non-pertinent) domains. Hence, we introduce Specialized Embedding Approximation (SEA) to train a student featurizer to approximate the teacher’s embedding manifold for a given target domain. We demonstrate the feasibility of SEA in the context of acoustic event classification for urban noise monitoring and show that leveraging a dataset related to this target domain not only improves the baseline performance of the original embedding model but also yields competitive students with >1 order of magnitude lesser storage and activation memory. We further investigate the impact of using random and informed sampling techniques for dimensionality reduction in SEA.
Domain Expansion in DNN-Based Acoustic Models for Robust Speech Recognition
Training acoustic models with sequentially incoming data –
while both leveraging new data and avoiding the forgetting effect
– is an essential obstacle to achieving human intelligence
level in speech recognition. An obvious approach to leverage
data from a new domain (e.g., new accented speech) is to first
generate a comprehensive dataset of all domains, by combining
all available data, and then use this dataset to retrain the
acoustic models. However, as the amount of training data
grows, storing and retraining on such a large-scale dataset
becomes practically impossible. To deal with this problem,
in this study, we study several domain expansion techniques
which exploit only the data of the new domain to build a
stronger model for all domains. These techniques are aimed at
learning the new domain with a minimal forgetting effect (i.e.,
they maintain original model performance). These techniques
modify the adaptation procedure by imposing new constraints
including (1) weight constraint adaptation (WCA): keeping
the model parameters close to the original model parameters;
(2) elastic weight consolidation (EWC): slowing down
training for parameters that are important for previously established
domains; (3) soft KL-divergence (SKLD): restricting
the KL-divergence between the original and the adapted
model output distributions; and (4) hybrid SKLD-EWC: incorporating
both SKLD and EWC constraints. We evaluate
these techniques in an accent adaptation task in which we
adapt a deep neural more »
- Award ID(s):
- 2016725
- Publication Date:
- NSF-PAR ID:
- 10180047
- Journal Name:
- IEEE ASRU-2019: Automatic Speech Recognition and Understanding Workshop
- Page Range or eLocation-ID:
- 107 to 113
- Sponsoring Org:
- National Science Foundation
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We present a simple approach to improve direct speech-to-text translation (ST) when the source language is low-resource: we pre-train the model on a high-resource automatic speech recognition (ASR) task, and then fine-tune its parameters for ST. We demonstrate that our approach is effective by pre-training on 300 hours of English ASR data to improve SpanishEnglish ST from 10.8 to 20.2 BLEU when only 20 hours of Spanish-English ST training data are available. Through an ablation study, we find that the pre-trained encoder (acoustic model) accounts for most of the improvement, despite the fact that the shared language in these tasks is the target language text, not the source language audio. Applying this insight, we show that pre-training on ASR helps ST even when the ASR language differs from both source and target ST languages: pre-training on French ASR also improves Spanish-English ST. Finally, we show that the approach improves performance on a true low-resource task: pre-training on a combination of English ASR and French ASR improves Mboshi-French ST, where only 4 hours of data are available, from 3.5 to 7.1 BLEU.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1109/ASRU46091.2019.9003769
