Philip C. Woodland

Bio: Philip C. Woodland is an academic researcher from University of Cambridge. The author has contributed to research in topics: Word error rate & Language model. The author has an hindex of 58, co-authored 263 publications receiving 16570 citations. Previous affiliations of Philip C. Woodland include BT Group & Google.

Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models

Abstract: A method of speaker adaptation for continuous density hidden Markov models (HMMs) is presented. An initial speaker-independent system is adapted to improve the modelling of a new speaker by updating the HMM parameters. Statistics are gathered from the available adaptation data and used to calculate a linear regression-based transformation for the mean vectors. The transformation matrices are calculated to maximize the likelihood of the adaptation data and can be implemented using the forward–backward algorithm. By tying the transformations among a number of distributions, adaptation can be performed for distributions which are not represented in the training data. An important feature of the method is that arbitrary adaptation data can be used—no special enrolment sentences are needed. Experiments have been performed on the ARPA RM1 database using an HMM system with cross-word triphones and mixture Gaussian output distributions. Results show that adaptation can be performed using as little as 11 s of adaptation data, and that as more data is used the adaptation performance improves. For example, using 40 adaptation utterances, a 37% reduction in error from the speaker-independent system was achieved with supervised adaptation and a 32% reduction in unsupervised mode.

The HTK book

Abstract: 1 The Fundamentals of HTK 2 1.1 General Principles of HMMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Isolated Word Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Output Probability Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Baum-Welch Re-Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 Recognition and Viterbi Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.6 Continuous Speech Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.7 Speaker Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Tree-based state tying for high accuracy acoustic modelling

Abstract: The key problem to be faced when building a HMM-based continuous speech recogniser is maintaining the balance between model complexity and available training data. For large vocabulary systems requiring cross-word context dependent modelling, this is particularly acute since many such contexts will never occur in the training data. This paper describes a method of creating a tied-state continuous speech recognition system using a phonetic decision tree. This tree-based clustering is shown to lead to similar recognition performance to that obtained using an earlier data-driven approach but to have the additional advantage of providing a mapping for unseen triphones. State-tying is also compared with traditional model-based tying and shown to be clearly superior. Experimental results are presented for both the Resource Management and Wall Street Journal tasks.

Minimum Phone Error and I-smoothing for improved discriminative training

Abstract: In this paper we introduce the Minimum Phone Error (MPE) and Minimum Word Error (MWE) criteria for the discriminative training of HMM systems. The MPE/MWE criteria are smoothed approximations to the phone or word error rate respectively. We also discuss I-smoothing which is a novel technique for smoothing discriminative training criteria using statistics for maximum likelihood estimation (MLE). Experiments have been performed on the Switchboard/Call Home corpora of telephone conversations with up to 265 hours of training data. It is shown that for the maximum mutual information estimation (MMIE) criterion, I-smoothing reduces the word error rate (WER) by 0.4% absolute over the MMIE baseline. The combination of MPE and I-smoothing gives an improvement of 1 % over MMIE and a total reduction in WER of 4.8% absolute over the original MLE system.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

Abstract: 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 coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition benchmarks, sometimes by a large margin. This article provides an overview of this progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.

A neural probabilistic language model

Abstract: A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen during training. Traditional but very successful approaches based on n-grams obtain generalization by concatenating very short overlapping sequences seen in the training set. We propose to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences. The model learns simultaneously (1) a distributed representation for each word along with (2) the probability function for word sequences, expressed in terms of these representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar (in the sense of having a nearby representation) to words forming an already seen sentence. Training such large models (with millions of parameters) within a reasonable time is itself a significant challenge. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach significantly improves on state-of-the-art n-gram models, and that the proposed approach allows to take advantage of longer contexts.

The Kaldi Speech Recognition Toolkit

Abstract: We describe the design of Kaldi, a free, open-source toolkit for speech recognition research. Kaldi provides a speech recognition system based on finite-state automata (using the freely available OpenFst), together with detailed documentation and a comprehensive set of scripts for building complete recognition systems. Kaldi is written is C++, and the core library supports modeling of arbitrary phonetic-context sizes, acoustic modeling with subspace Gaussian mixture models (SGMM) as well as standard Gaussian mixture models, together with all commonly used linear and affine transforms. Kaldi is released under the Apache License v2.0, which is highly nonrestrictive, making it suitable for a wide community of users.

Librispeech: An ASR corpus based on public domain audio books

Abstract: This paper introduces a new corpus of read English speech, suitable for training and evaluating speech recognition systems. The LibriSpeech corpus is derived from audiobooks that are part of the LibriVox project, and contains 1000 hours of speech sampled at 16 kHz. We have made the corpus freely available for download, along with separately prepared language-model training data and pre-built language models. We show that acoustic models trained on LibriSpeech give lower error rate on the Wall Street Journal (WSJ) test sets than models trained on WSJ itself. We are also releasing Kaldi scripts that make it easy to build these systems.