Automatic Speech Recognition

By John Dines. 2005

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Citations in the corpus

One-shot Learning with Memory-Augmented Neural Networks (2016, background): “This approach has achieved impressive results on many large-scale supervised tasks with raw sensory input, such as image classiﬁcation (He et al., 2015), speech recognition (Yu & Deng, 2012), and games (Mnih et al., 2015; Silver et al., 2016).”
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017, background): “Deep Neural Networks Neural networks and deep learning [4, 38, 39] have gained numerous success in the fields such as compute vision [13, 40], speech recognition [41, 42], and natural language processing [43].”
Compositional Generalization for Primitive Substitutions (2019, background): “In recent years, deep neural networks have made many breakthroughs in various problems (LeCun et al., 2015; Krizhevsky et al., 2012; Yu and Deng, 2012; He et al., 2016; Wu and et al, 2016).”
JHU ASpIRE system: Robust LVCSR with TDNNS, iVector adaptation and RNN-LMS (2015, methodology): “We show experimental results on the ASpIRE far-ﬁeld speech recognition challenge held by IARPA [8].”
Meta-Learning with Memory-Augmented Neural Networks (2016, background): “Copyright 2016 by the author(s). nition (Yu & Deng, 2012), and games (Mnih et al., 2015; Silver et al., 2016).”
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition (2018, background): “, GMM-HMM [25]), not the DNN behind today’s IVC systems.”
Multimodal Speech Emotion Recognition Using Audio and Text (2018, background): “We assume that speech transcripts can be extracted from audio signals with high accuracy, given the advancement of ASR technologies [8].”
The Tree Ensemble Layer: Differentiability meets Conditional Computation (2020, background): “This is an area in which neural networks (NNs) excel, especially in speech and image recognition applications (Bengio et al., 2013; He et al., 2015; Yu & Deng, 2016).”
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2023, background): “For example, image captioning (Johnson et al., 2016; Wang et al., 2022a), automatic speech recognition (Yu and Deng, 2016; Radford et al., 2022), text-to-image generation (Ramesh et al., 2021, 2022; Saharia et al., 2022; Rombach et al., 2022), etc. Inspired by the success of unified…”