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Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR

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Ruchao Fan, Amber Afshan, Abeer Alwan

Audio and Speech Processing · Submitted 12 Feb 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim When applied to BLSTM, however, APC is not as competitive as SPT, but our proposed Bi-APC has comparable improvements to SPT.

how it is stated · asserted 1.00 read the record →

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arXiv ID2102.06816 (version 1)
Pith ID6BV6ONV6, Pith's permanent record of this paper and its review
CitationAbeer Alwan, Amber Afshan, Ruchao Fan. "Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR." arXiv:2102.06816 (2021). https://pith.science/paper/6BV6ONV6
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What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.41 · human participants 0.17 · controlled comparison 0.92 · randomized assignment 0.01 · states quantitative result 0.10 · compares with prior results 0.97 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We present a bidirectional unsupervised model pre-training (UPT) method and apply it to children's automatic speech recognition (ASR). An obstacle to improving child ASR is the scarcity of child speech databases. A common approach to alleviate this problem is model pre-training using data from adult speech. Pre-training can be done using supervised (SPT) or unsupervised methods, depending on the availability of annotations. Typically, SPT performs better. In this paper, we focus on UPT to address the situations when pre-training data are unlabeled. Autoregressive predictive coding (APC), a UPT method, predicts frames from only one direction, limiting its use to uni-directional pre-training. Conventional bidirectional UPT methods, however, predict only a small portion of frames. To extend the benefits of APC to bi-directional pre-training, Bi-APC is proposed. We then use adaptation techniques to transfer knowledge learned from adult speech (using the Librispeech corpus) to child speech (OGI Kids corpus). LSTM-based hybrid systems are investigated. For the uni-LSTM structure, APC obtains similar WER improvements to SPT over the baseline. When applied to BLSTM, however, APC is not as competitive as SPT, but our proposed Bi-APC has comparable improvements to SPT.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present a bidirectional unsupervised model pre-training (UPT) method and apply it to children's automatic speech recognition (ASR). contribution 1.00 0.95 0.15 asserted 0.99
s1 An obstacle to improving child ASR is the scarcity of child speech databases. background 0.90 0.08 0.02 asserted 1.00
s2 A common approach to alleviate this problem is model pre-training using data from adult speech. background 1.00 0.04 0.02 asserted 0.99
s3 Pre-training can be done using supervised (SPT) or unsupervised methods, depending on the availability of annotations. background 1.00 0.05 0.02 asserted 0.98
s4 Typically, SPT performs better. background 1.00 0.07 0.03 asserted 0.92
s5 In this paper, we focus on UPT to address the situations when pre-training data are unlabeled. contribution 0.51 0.45 0.04 asserted 0.97
s6 Autoregressive predictive coding (APC), a UPT method, predicts frames from only one direction, limiting its use to uni-directional pre-training. background 0.69 0.08 0.03 asserted 1.00
s7 Conventional bidirectional UPT methods, however, predict only a small portion of frames. gap 0.61 0.09 0.03 asserted 1.00
s8 To extend the benefits of APC to bi-directional pre-training, Bi-APC is proposed. contribution 0.93 0.80 0.09 asserted 0.99
s9 We then use adaptation techniques to transfer knowledge learned from adult speech (using the Librispeech corpus) to child speech (OGI Kids corpus). method 0.95 0.91 0.05 asserted 0.97
s10 LSTM-based hybrid systems are investigated. method 0.96 0.67 0.02 asserted 0.71
s11 For the uni-LSTM structure, APC obtains similar WER improvements to SPT over the baseline. result 1.00 0.88 0.11 asserted 1.00
s12 When applied to BLSTM, however, APC is not as competitive as SPT, but our proposed Bi-APC has comparable improvements to SPT. result 1.00 0.93 0.60 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.93.

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present a bidirectional unsupervised model pre-training (UPT) method and apply it to children's automatic speech recognition (ASR). An obstacle to improving child ASR is the scarcity of child speech databases. A common approach to alleviate this problem is model pre-training using data from adult speech. Pre-training can be done using supervised (SPT) or unsupervised methods, depending on the availability of annotations. Typically, SPT performs better. In this paper, we focus on UPT to address the situations when pre-training data are unlabeled. Autoregressive predictive coding (APC), a UPT method, predicts frames from only one direction, limiting its use to uni-directional pre-training. Conventional bidirectional UPT methods, however, predict only a small portion of frames. To extend the benefits of APC to bi-directional pre-training, Bi-APC is proposed. We then use adaptation techniques to transfer knowledge learned from adult speech (using the Librispeech corpus) to child speech (OGI Kids corpus). LSTM-based hybrid systems are investigated. For the uni-LSTM structure, APC obtains similar WER improvements to SPT over the baseline. When applied to BLSTM, however, APC is not as competitive as SPT, but our proposed Bi-APC has comparable improvements to SPT.

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