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Sequence-Level Self-Learning with Multiple Hypotheses
2020
Interspeech 2020
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In this work, we develop new self-learning techniques with an attention-based sequence-to-sequence (seq2seq) model for automatic speech recognition (ASR). ...
The seq2seq network is updated through the MTL framework so as to find the common representation that can cover multiple hypotheses. ...
Proposed self-learning method
Loss function approximation with multiple hypotheses In many cases, the best ASR output contains errors. ...
doi:10.21437/interspeech.2020-2020
dblp:conf/interspeech/KumataniDGGELZ20
fatcat:ok6cdndf2jbubh4jhpqfjqesei
Learning from Observing: Vision and POIROT - Using Metareasoning for Self Adaptation
2010
2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshop
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We present a cognitive architecture that heavily utilizes metareasoning for self adaptation,. ...
We also discuss how this architecture is applied in the POIROT system, which learns web services workflow from "observing" a small number of expert examples. ...
with respect to additional examples, and to self-evaluate the quality of its component's outputs. ...
doi:10.1109/sasow.2010.61
dblp:conf/saso/BursteinBFLR10
fatcat:s2hgkt7wxfgzteqx274al5nvsq
Poetry to Prose Conversion in Sanskrit as a Linearisation Task: A Case for Low-Resource Languages
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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The first pretraining step learns task specific token embeddings from pretrained embeddings. In the next step, we generate multiple hypotheses for possible word arrangements of the input . ...
The word ordering in a Sanskrit verse is often not aligned with its corresponding prose order. Conversion of the verse to its corresponding prose helps in better comprehension of the construction. ...
Pretraining Step 2 -Self-Attention Based Word-Ordering (SAWO): SAWO allows us to generate multiple permutations of words as hypotheses, which can be used as input to a seq2seq model. ...
doi:10.18653/v1/p19-1111
dblp:conf/acl/KrishnaSSCSG19
fatcat:vfyr3thuzndipjjgtbe5mjqp5e
MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation
[article]
2022
arXiv
pre-print
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representations; (ii) Model self-hypothesis communication, merge multiple hypotheses into a single converged representation and then partition it into several diverged hypotheses; (iii) Learn cross-hypothesis ...
To relieve this limitation, we propose a Multi-Hypothesis Transformer (MHFormer) that learns spatio-temporal representations of multiple plausible pose hypotheses. ...
Suppose there are M different hypotheses and L 1 layers in the MHG, it takes a sequence of 2D poses X∈R N ×J×2 with N video frames and J body joints as input and outputs multiple hypotheses [X 1 L1 , X ...
arXiv:2111.12707v4
fatcat:cvhsefnvj5clpaanwkdzcnsxj4
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition
[article]
2020
arXiv
pre-print
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In this paper we present state-of-the-art (SOTA) performance on the LibriSpeech corpus with two novel neural network architectures, a multistream CNN for acoustic modeling and a self-attentive simple recurrent ...
In the hybrid ASR framework, the multistream CNN acoustic model processes an input of speech frames in multiple parallel pipelines where each stream has a unique dilation rate for diversity. ...
All of our self-attentivce SRU language models are trained at the utterance level (i.e., the model does not leverage any context past sentence boundaries), with a maximum sequence length of 275 tokens. ...
arXiv:2005.10469v1
fatcat:2w2bphgbjfhzzinnnshf4knnba
ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition
2020
Interspeech 2020
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We further improve the performance via N -best rescoring using a 24-layer self-attentive SRU language model, achieving WERs of 1.75% on test-clean and 4.46% on test-other. ...
In the hybrid ASR framework, the multistream CNN acoustic model processes an input of speech frames in multiple parallel pipelines where each stream has a unique dilation rate for diversity. ...
All of our self-attentivce SRU language models are trained at the utterance level (i.e., the model does not leverage any context past sentence boundaries), with a maximum sequence length of 275 tokens. ...
doi:10.21437/interspeech.2020-2947
dblp:conf/interspeech/PanSWH0M20
fatcat:pfvnsjbilfdzhmadi24doeql5e
Examining self-efficacy during learning: variability and relations to behavior, performance, and learning
2014
Metacognition and Learning
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Findings suggest that self-efficacy varies during learning, that students consider multiple aspects of performance to inform their efficacy judgments, and that changes in efficacy influence self-regulated ...
Self-regulated learning (SRL) theorists propose that learners' motivations and cognitive and metacognitive processes interact dynamically during learning, yet researchers typically measure motivational ...
Self efficacy as a dynamic component of SRL Because our research hypotheses are predicated on the assumption that learners' level of selfefficacy changes over a learning task, we first must confirm that ...
doi:10.1007/s11409-014-9127-x
fatcat:hzhox4lpizevlmtq5n3rwedvly
The Influence of WebCT Information Technology and Structure of Instruction on Students Academic Performance
2007
Americas Conference on Information Systems
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The purpose of this research is to investigate the influence of WebCT Information Technology, students' perceived computer self-efficacy, students' motivation to learn and the degree of course structure ...
Hiltz also found that levels of maturity, degree of effort, levels of academic ability and motivation all correlate positively with learning outcomes. ...
V) Data Analysis : The author used multiple regression procedure to test the simultaneous influence of the hypothesized independent variables on students' academic outcomes and to validate the stated hypotheses ...
dblp:conf/amcis/Taskov07
fatcat:wzqjgda3wne5znhuamc5eqksdq
ADAPTIVE GUIDANCE: ENHANCING SELF-REGULATION, KNOWLEDGE, AND PERFORMANCE IN TECHNOLOGY-BASED TRAINING
2002
Personnel Psychology
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In web-based training, for example, individuals can use hyper-links and menus to customize the material to which they attend, determine the sequence by which they learn, and control the amount of time ...
Yet, today's technologically based training systems often provide individuals with significant control over their learning (Brown, 2001) . ...
Consistent with the idea of sequencing individuals' learning process, trainees with a stronger foundation of basic skills and knowledge early in training displayed higher levels of strategic skills and ...
doi:10.1111/j.1744-6570.2002.tb00111.x
fatcat:whrtvbviq5g5xmo7w3wzqz6j3y
Semi-Supervised Speech Recognition via Graph-based Temporal Classification
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2010.15653v1 fatcat:sy6n4dnqd5euvjxtjaksqgfgxq
Semi-supervised learning has demonstrated promising results in automatic speech recognition (ASR) by self-training using a seed ASR model with pseudo-labels generated for unlabeled data. ...
In this setup, GTC is used to learn not only a temporal alignment, similarly to CTC, but also a label alignment to obtain the optimal pseudo-label sequence from the weighted graph. ...
Note that self-training with lattice-based supervision was also proposed in [22] using a hybrid ASR system and the LF-MMI objective in order to incorporate frame-level confidence scores and alternate ...
arXiv:2010.15653v2
fatcat:dqdntftdrzfrnh6upmc5yjsnz4
Capturing Sequences of Learners' Self-Regulatory Interactions With Instructional Material During Game-Based Learning Using Auto-Recurrence Quantification Analysis
2022
Frontiers in Psychology
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learning technologies to scaffold self-regulation during game play. ...
Through hierarchical modeling, analyses suggested that greater dwell times and learning gains were associated with more predictable sequences of interaction with instructional materials. ...
For this third research question, we hypothesized that learners with more repetitive eye gaze sequences would be present in learners with restricted agency and related with higher learning gains. ...
doi:10.3389/fpsyg.2022.813677
pmid:35712220
pmcid:PMC9197103
fatcat:h6fs6mjazzebtmmvomwxk25htm
Instance-Aware Predictive Navigation in Multi-Agent Environments
[article]
2021
arXiv
pre-print
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In this work, we aim to achieve efficient end-to-end learning of driving policies in dynamic multi-agent environments. ...
We design a sequential action sampling strategy to better leverage predicted states on both scene-level and instance-level. ...
MEP predicts the inter-agent events along with the visual structure as well as their uncertainty scores to enable multiple hypotheses forecasting. ...
arXiv:2101.05893v1
fatcat:tyogyo2dlbbadbzauqiq6t6g5y
Do Video Modeling and Metacognitive Prompts Improve Self-Regulated Scientific Inquiry?
2022
Educational Psychology Review
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Our findings show that video modeling examples are a promising instructional method for supporting inquiry learning on both the process and the learning outcomes level. ...
Process mining revealed that in the VM conditions these processes occurred in unique sequences and that self-regulation processes had many self-loops. ...
In line with Bannert et al. (2015) , we hypothesized that the VMP condition would outperform the VM condition (H3b). ...
doi:10.1007/s10648-021-09652-3
fatcat:panq42qkj5h4lilenszxh4t5ra
Progressive Joint Modeling in Unsupervised Single-Channel Overlapped Speech Recognition
2018
IEEE/ACM Transactions on Audio Speech and Language Processing
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The improvement comes from better model generalization, training efficiency and the sequence level linguistic knowledge integration. ...
learning and a discriminative training criterion. ...
ACKNOWLEDGMENT We thank Chris Basoglu, Frank Seide for their invaluable assistance with CNTK; Mike Seltzer, Takuya Yoshioka, Hakan Erdogan and Andreas Stolcke for many helpful conversations. ...
doi:10.1109/taslp.2017.2765834
fatcat:2e7p7bwqsvhk5beu6lei762ivi
Learning Representations for Predicting Future Activities
[article]
2019
arXiv
pre-print
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In this work, we address future prediction at the abstract level of activities. We propose a network module for learning embeddings of the environment's dynamics in a self-supervised way. ...
To take the ambiguities and high variances in the future activities into account, we use a multi-hypotheses scheme that can represent multiple futures. ...
Therefore, we propose learning multiple hypotheses with their uncertainties, similar to multihypotheses networks (MHN) [16, 25, 6, 36] . ...
arXiv:1905.03578v1
fatcat:6kfyojpbkzedxjm2dz3pyxi3hi
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