Search Results for author:
Found 22 papers, 11 papers with code
Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine
Straightforward application of Deep Belief Nets (DBNs) to acoustic modeling produces a rich distributed representation of speech data that is useful for recognition and yields impressive results on the speaker-independent TIMIT phone recognition task.
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input.
Speech Recognition with Deep Recurrent Neural Networks
Recurrent neural networks (RNNs) are a powerful model for sequential data.
Ranked #18 on
Speech Recognition
on TIMIT
Improvements to deep convolutional neural networks for LVCSR
We find that with these improvements, particularly with fMLLR and dropout, we are able to achieve an additional 2-3% relative improvement in WER on a 50-hour Broadcast News task over our previous best CNN baseline.
Blending LSTMs into CNNs
We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Neuro-Symbolic Program Synthesis
While achieving impressive results, these approaches have a number of important limitations: (a) they are computationally expensive and hard to train, (b) a model has to be trained for each task (program) separately, and (c) it is hard to interpret or verify the correctness of the learnt mapping (as it is defined by a neural network).
Memory-augmented Attention Modelling for Videos
We present a method to improve video description generation by modeling higher-order interactions between video frames and described concepts.
Sequence Modeling via Segmentations
The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks.
RobustFill: Neural Program Learning under Noisy I/O
Recently, two competing approaches for automatic program learning have received significant attention: (1) neural program synthesis, where a neural network is conditioned on input/output (I/O) examples and learns to generate a program, and (2) neural program induction, where a neural network generates new outputs directly using a latent program representation.
Deep API Programmer: Learning to Program with APIs
We then present a novel neural synthesis algorithm to search for programs in the DSL that are consistent with a given set of examples.
Mean Actor Critic
We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action continuous-state reinforcement learning.
Ranked #1 on
Continuous Control
on Cart Pole (OpenAI Gym)
Differentiable Greedy Networks
Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization.
Transformer-based Acoustic Modeling for Hybrid Speech Recognition
We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition.
Ranked #23 on
Speech Recognition
on LibriSpeech test-other
(using extra training data)
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.
Ranked #3 on
Open-Domain Question Answering
on ELI5
Enforcing Encoder-Decoder Modularity in Sequence-to-Sequence Models
Inspired by modular software design principles of independence, interchangeability, and clarity of interface, we introduce a method for enforcing encoder-decoder modularity in seq2seq models without sacrificing the overall model quality or its full differentiability.
Effectiveness of self-supervised pre-training for speech recognition
We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization.
Libri-Light: A Benchmark for ASR with Limited or No Supervision
Additionally, we provide baseline systems and evaluation metrics working under three settings: (1) the zero resource/unsupervised setting (ABX), (2) the semi-supervised setting (PER, CER) and (3) the distant supervision setting (WER).
Ranked #1 on
Speech Recognition
on Libri-Light test-other
(ABX-within metric)
Large scale weakly and semi-supervised learning for low-resource video ASR
Many semi- and weakly-supervised approaches have been investigated for overcoming the labeling cost of building high quality speech recognition systems.
wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.
Ranked #1 on
Speech Recognition
on TIMIT
(using extra training data)
Unsupervised Cross-lingual Representation Learning for Speech Recognition
This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages.
