Patrick Haffner

Bio: Patrick Haffner is an academic researcher from AT&T Labs. The author has contributed to research in topics: Support vector machine & Speaker recognition. The author has an hindex of 32, co-authored 97 publications receiving 42604 citations. Previous affiliations of Patrick Haffner include Nuance Communications & Carnegie Mellon University.

Combining Frame and Segment Level Processing via Temporal Pooling for Phonetic Classification.

Abstract: We propose a simple, yet novel, multi-layer model for the problem of phonetic classification. Our model combines a frame level transformation of the acoustic signal with a segment level phone classification. Our key contribution is the study of new temporal pooling strategies that interface these two levels, determining how frame scores are converted into segment scores. On the TIMIT benchmark, we match the best performance obtained using a single classifier. Diversity in pooling strategies is further used to generate candidate classifiers with complementary performance characteristics, which perform even better as an ensemble. Without the use of any phonetic knowledge, our ensemble model achieves a 16.96% phone classification error. While our data-driven approach is exhaustive, the combinatorial inflation is limited to the smaller segmental half of the system.

Semantic data mining of short utterances

Abstract: This paper introduces a methodology for speech data mining along with the tools that the methodology requires. We show how they increase the productivity of the analyst who seeks relationships among the contents of multiple utterances and ultimately must link some newly discovered context into testable hypotheses about new information. While, in its simplest form, one can extend text data mining to speech data mining by using text tools on the output of a speech recognizer, we have found that it is not optimal. We show how data mining techniques that are typically applied to text should be modified to enable an analyst to do effective semantic data mining on a large collection of short speech utterances. For the purposes of this paper, we examine semantic data mining in the context of semantic parsing and analysis in a specific situation involving the solution of a business problem that is known to the analyst. We are not attempting a generic semantic analysis of a set of speech. Our tools and methods allow the analyst to mine the speech data to discover the semantics that best cover the desired solution. The coverage, in this case, yields a set of Natural Language Understanding (NLU) classifiers that serve as testable hypotheses.

Multi-class classification learning on several processors

Abstract: The time taken to learn a model from training examples is often unacceptable. For instance, training language understanding models with Adaboost or SVMs can take weeks or longer based on numerous training examples. Parallelization through the use of multiple processors may improve learning speed. The disclosure describes effective systems for distributed multiclass classification learning on several processors. These systems are applicable to multiclass models where the training process may be split into training of independent binary classifiers.

Deep Clustering with Measure Propagation.

Abstract: Deep models have improved state-of-the-art for both supervised and unsupervised learning. For example, deep embedded clustering (DEC) has greatly improved the unsupervised clustering performance, by using stacked autoencoders for representation learning. However, one weakness of deep modeling is that the local neighborhood structure in the original space is not necessarily preserved in the latent space. To preserve local geometry, various methods have been proposed in the supervised and semi-supervised learning literature (e.g., spectral clustering and label propagation) using graph Laplacian regularization. In this paper, we combine the strength of deep representation learning with measure propagation (MP), a KL-divergence based graph regularization method originally used in the semi-supervised scenario. The main assumption of MP is that if two data points are close in the original space, they are likely to belong to the same class, measured by KL-divergence of class membership distribution. By taking the same assumption in the unsupervised learning scenario, we propose our Deep Embedded Clustering Aided by Measure Propagation (DECAMP) model. We evaluate DECAMP on short text clustering tasks. On three public datasets, DECAMP performs competitively with other state-of-the-art baselines, including baselines using additional data to generate word embeddings used in the clustering process. As an example, on the Stackoverflow dataset, DECAMP achieved a clustering accuracy of 79%, which is about 5% higher than all existing baselines. These empirical results suggest that DECAMP is a very effective method for unsupervised learning.

Deep learning

Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Gradient-based learning applied to document recognition

Abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.

Going deeper with convolutions

Abstract: We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

The Nature of Statistical Learning Theory

Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.

Generative Adversarial Nets

Abstract: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.