Andrew Senior - IEEE Xplore Author Profile

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The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem – unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) we compare two models for lip reading, on...Show More
The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem – unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) a Watch, Listen, Attend and Spell (WLAS) ...Show More
Multichannel automatic speech recognition (ASR) systems commonly separate speech enhancement, including localization, beamforming, and postfiltering, from acoustic modeling. In this paper, we perform multichannel enhancement jointly with acoustic modeling in a deep neural network framework. Inspired by beamforming, which leverages differences in the fine time structure of the signal at different m...Show More
We present a recipe for training acoustic models with context dependent (CD) phones from scratch using recurrent neural networks (RNNs). First, we use the connectionist temporal classification (CTC) technique to train a model with context independent (CI) phones directly from the written-domain word transcripts by aligning with all possible phonetic verbalizations. Then, we devise a mechanism to g...Show More
This paper describes a series of experiments to extend the application of Context-Dependent (CD) long short-term memory (LSTM) recurrent neural networks (RNNs) trained with Connectionist Temporal Classification (CTC) and sMBR loss. Our experiments, on a noisy, reverberant voice search task, include training with alternative pronunciations and the application to child speech recognition; combinatio...Show More
Multichannel ASR systems commonly use separate modules to perform speech enhancement and acoustic modeling. In this paper, we present an algorithm to do multichannel enhancement jointly with the acoustic model, using a raw waveform convolutional LSTM deep neural network (CLDNN). We will show that our proposed method offers ~5% relative improvement in WER over a log-mel CLDNN trained on multiple ch...Show More
Both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) have shown improvements over Deep Neural Networks (DNNs) across a wide variety of speech recognition tasks. CNNs, LSTMs and DNNs are complementary in their modeling capabilities, as CNNs are good at reducing frequency variations, LSTMs are good at temporal modeling, and DNNs are appropriate for mapping features to a more s...Show More
We explore alternative acoustic modeling techniques for large vocabulary speech recognition using Long Short-Term Memory recurrent neural networks. For an acoustic frame labeling task, we compare the conventional approach of cross-entropy (CE) training using fixed forced-alignments of frames and labels, with the Connectionist Temporal Classification (CTC) method proposed for labeling unsegmented s...Show More
Long Short Term Memory Recurrent Neural Networks (LSTM RNNs), combined with hidden Markov models (HMMs), have recently been show to outperform other acoustic models such as Gaussian mixture models (GMMs) and deep neural networks (DNNs) for large scale speech recognition. We argue that using multi-state HMMs with LSTM RNN acoustic models is an unnecessary vestige of GMM-HMM and DNN-HMM modelling si...Show More
Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) are the two most common types of acoustic models used in statistical parametric approaches for generating low-level speech waveforms from high-level symbolic inputs via intermediate acoustic feature sequences. However, these models have their limitations in representing complex, nonlinear relationships between the speech generation inp...Show More
Automatic speech recognition (ASR) systems are used daily by millions of people worldwide to dictate messages, control devices, initiate searches or to facilitate data input in small devices. The user experience in these scenarios depends on the quality of the speech transcriptions and on the responsiveness of the system. For multilingual users, a further obstacle to natural interaction is the mon...Show More
Statistical parametric speech synthesis (SPSS) using deep neural networks (DNNs) has shown its potential to produce naturally-sounding synthesized speech. However, there are limitations in the current implementation of DNN-based acoustic modeling for speech synthesis, such as the unimodal nature of its objective function and its lack of ability to predict variances. To address these limitations, t...Show More
We investigate the use of large state inventories and the softplus nonlinearity for on-device neural network based mobile speech recognition. Large state inventories are achieved by less aggressive context-dependent state tying, and made possible by using a bottleneck layer to contain the number of parameters. We investigate alternative approaches to the bottleneck layer, demonstrate the superiori...Show More
While deep neural networks (DNNs) have become the dominant acoustic model (AM) for speech recognition systems, they are still dependent on Gaussian mixture models (GMMs) for alignments both for supervised training and for context dependent (CD) tree building. Here we explore bootstrapping DNN AM training without GMM AMs and show that CD trees can be built with DNN alignments which are better match...Show More
We propose providing additional utterance-level features as inputs to a deep neural network (DNN) to facilitate speaker, channel and background normalization. Modifications of the basic algorithm are developed which result in significant reductions in word error rates (WERs). The algorithms are shown to combine well with speaker adaptation by backpropagation, resulting in a 9% relative WER reducti...Show More
This paper explores asynchronous stochastic optimization for sequence training of deep neural networks. Sequence training requires more computation than frame-level training using pre-computed frame data. This leads to several complications for stochastic optimization, arising from significant asynchrony in model updates under massive parallelization, and limited data shuffling due to utterance-ch...Show More
YouTube is a highly visited video sharing website where over one billion people watch six billion hours of video every month. Improving accessibility to these videos for the hearing impaired and for search and indexing purposes is an excellent application of automatic speech recognition. However, YouTube videos are extremely challenging for automatic speech recognition systems. Standard adapted Ga...Show More
Today's speech recognition technology is mature enough to be useful for many practical applications. In this context, it is of paramount importance to train accurate acoustic models for many languages within given resource constraints such as data, processing power, and time. Multilingual training has the potential to solve the data issue and close the performance gap between resource-rich and res...Show More
Recent deep neural network systems for large vocabulary speech recognition are trained with minibatch stochastic gradient descent but use a variety of learning rate scheduling schemes. We investigate several of these schemes, particularly AdaGrad. Based on our analysis of its limitations, we propose a new variant `AdaDec' that decouples long-term learning-rate scheduling from per-parameter learnin...Show More
Deep neural networks have recently become the gold standard for acoustic modeling in speech recognition systems. The key computational unit of a deep network is a linear projection followed by a point-wise non-linearity, which is typically a logistic function. In this work, we show that we can improve generalization and make training of deep networks faster and simpler by substituting the logistic...Show More
Conventional approaches to statistical parametric speech synthesis typically use decision tree-clustered context-dependent hidden Markov models (HMMs) to represent probability densities of speech parameters given texts. Speech parameters are generated from the probability densities to maximize their output probabilities, then a speech waveform is reconstructed from the generated parameters. This a...Show More
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) 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. An alternative way to evaluate the fit is to use a feed-forward neural network that takes several frames of...Show More
This paper explores a novel large margin approach to learning a linear transform for dimensionality reduction in speech recognition. The method assumes a trained Gaussian mixture model for each class to be discriminated and trains a dimensionality-reducing linear transform with respect to the fixed model, optimizing a hinge loss on the difference between the distance to the nearest in- and out-of-...Show More
Optical character recognition is carried out using techniques borrowed from statistical machine translation. In particular, the use of multiple simple feature functions in linear combination, along with minimum-error-rate training, integrated decoding, and N-gram language modeling is found to be remarkably effective, across several scripts and languages. Results are presented using both synthetic ...Show More
This paper presents mechanisms for privacy protection in a distributed, multicamera surveillance system. The design choices and alternatives for providing privacy protection while delivering meaningful surveillance data for security and retail environments are described, followed by performance metrics to evaluate the effectiveness of privacy protection measures and experiments to evaluate these i...Show More