Towards End-To-End Speech Recognition with Recurrent Neural Networks

By Alex Graves, Navdeep Jaitly. 2014

2,301 citations recorded by the source corpus.

Subfields: Natural Language Processing, Model Architectures

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Citations in the corpus

Neural GPUs Learn Algorithms (2015, background): “…networks with such cells can learn very complex tasks in an end-to-end manner, such as translation (Sutskever et al., 2014; Bahdanau et al., 2014; Cho et al., 2014), parsing (Vinyals & Kaiser et al., 2015), speech recognition (Chan et al., 2016) or image caption generation (Vinyals et al., 2014).”
Neural Turing Machines (2014, background): “…variable-length structures, they have recently been used in a variety of cognitive problems, including speech recognition (Graves et al., 2013; Graves and Jaitly, 2014), text generation (Sutskever et al., 2011), handwriting generation (Graves, 2013) and machine translation (Sutskever et al.,…”
Imputer: Sequence Modelling via Imputation and Dynamic Programming (2020, methodology): “Three popular types of generative models have been developed for end-to-end speech recognition applications: CTC (Graves et al., 2006; Graves & Jaitly, 2014), RNN-T (Graves, 2012; Graves et al., 2013) and seq2seq (Chan et al., 2016; Bahdanau et al., 2016a).”
End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures (2019, neutral): “Nonetheless, there have been increasingly competitive end-to-end results trained with CTC [15, 1], ASG [8, 54], LF-MMI [17], sequence-to-sequence [4, 5], transduction [41, 22], and differentiable decoding [9].”
Recurrent Neural Networks for Semantic Instance Segmentation (2017, background): “Given enough training data and computational power, a great variety of automatic tasks such as object recognition [8], machine translation [9], speech recognition [10] or self-driving cars [11] have seen a boost of performance thanks to models trained end-to-end, i.”
Generative Moment Matching Networks (2015, background): “…achieving impressive results on a number of tasks such as object recognition (Krizhevsky et al., 2012; Sermanet et al., 2014; Szegedy et al., 2014), speech recognition (Graves & Jaitly, 2014; Hinton et al., 2012a), image caption generation (Vinyals et al., 2014; Fang et al., 2014; Preliminary work.”
Generative Language Modeling for Automated Theorem Proving (2020, background): “Artiﬁcial neural networks have enjoyed a spectacularly successful decade, having made considerable advances in computer vision [1, 2], translation [3, 4, 5], speech recognition [6, 7], image generation [8, 9, 10, 11, 12], game playing [13, 14, 15], and robotics [16, 17].”
mSLAM: Massively multilingual joint pre-training for speech and text (2022, methodology): “Second, we apply a CTC loss (Graves et al., 2006; Graves & Jaitly, 2014) on the speech portion of the paired input, using the characterlevel transcript as the target.”
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10, 000 Hours of Transcribed Audio (2021, background): “Various system architectures, from hybrid [1] to end-to-end [2], are proposed, and state-of-the-art results on standard benchmarks are being frequently updated.”
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network (2021, neutral): “End-to-end speech recognition models [1, 2, 3] have seen remarkable success in recent years [4].”
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition (2019, methodology): “Deep Learning has been applied successfully to Automatic Speech Recognition (ASR) [1], where the main focus of research has been designing better network architectures, for example, DNNs [2], CNNs [3], RNNs [4] and end-to-end models [5, 6, 7].”
Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin (2015, background): “The Deep Speech 2 ASR pipeline approaches or exceeds the accuracy of Amazon Mechanical Turk human workers on several benchmarks, works in multiple languages with little modiﬁcation, and is deployable in a production setting.”
Transformers with convolutional context for ASR (2019, background): “Advances in alignment-free sequence-level loss functions like CTC and ASG [3, 4] enabled easier training with letters as output units [5, 6].”
Long-term recurrent convolutional networks for visual recognition and description (2014, methodology): “We use the LSTM unit as described in [46] (Figure 2, right), which is a slight simplification of the one described in [10].”
Listen, attend and spell: A neural network for large vocabulary conversational speech recognition (2015, methodology): “In this section, we will formally describe LAS which accepts acoustic features as inputs and emits English characters as outputs.”
Attention-Based Models for Speech Recognition (2015, methodology): “In the future, we expect this model to be used to directly recognize text from speech [10, 17], in which case it may become important to incorporate a monolingual language model to the ARSG architecture [26].”
Deep Speech: Scaling up end-to-end speech recognition (2014, methodology): “For example, Graves et al. [13] have previously introduced the “Connectionist Temporal Classiﬁcation” (CTC) loss function for scoring transcriptions produced by RNNs and, with LSTM networks, have previously applied this approach to speech [14].”
Unsupervised Learning of Video Representations using LSTMs (2015, methodology): “…(Hochreiter & Schmidhuber, 1997) have been used successfully to perform various supervised sequence learning tasks, such as speech recognition (Graves & Jaitly, 2014), machine translation (Sutskever et al., 2014; Cho et al., 2014), and caption generation for images (Vinyals et al., 2014).”
Towards Better Decoding and Language Model Integration in Sequence to Sequence Models (2016, neutral): “Our best result surpasses CTCbased networks [6] and matches the results of a DNN-HMM and CTC ensemble [23].”
Neural Programmer: Inducing Latent Programs with Gradient Descent (2015, background): “While there has been lot of work in augmenting neural networks with additional memory (Das et al., 1992a; Schmidhuber, 1993; Hochreiter & Schmidhuber, 1997; Graves et al., 2014; Weston et al., 2015; Kumar et al., 2015; Joulin & Mikolov, 2015), we are not aware of any other work that augments a neura”