Other affiliations: University of Iowa, Nuance Communications, Massachusetts Institute of Technology
Bio: Michael Picheny is an academic researcher from IBM. The author has contributed to research in topics: Language model & Word error rate. The author has an hindex of 57, co-authored 244 publications receiving 11759 citations. Previous affiliations of Michael Picheny include University of Iowa & Nuance Communications.
Papers published on a yearly basis
••01 Dec 2013
TL;DR: This work proposes to adapt deep neural network acoustic models to a target speaker by supplying speaker identity vectors (i-vectors) as input features to the network in parallel with the regular acoustic features for ASR, comparable in performance to DNNs trained on speaker-adapted features with the advantage that only one decoding pass is needed.
Abstract: We propose to adapt deep neural network (DNN) acoustic models to a target speaker by supplying speaker identity vectors (i-vectors) as input features to the network in parallel with the regular acoustic features for ASR. For both training and test, the i-vector for a given speaker is concatenated to every frame belonging to that speaker and changes across different speakers. Experimental results on a Switchboard 300 hours corpus show that DNNs trained on speaker independent features and i-vectors achieve a 10% relative improvement in word error rate (WER) over networks trained on speaker independent features only. These networks are comparable in performance to DNNs trained on speaker-adapted features (with VTLN and FMLLR) with the advantage that only one decoding pass is needed. Furthermore, networks trained on speaker-adapted features and i-vectors achieve a 5-6% relative improvement in WER after hessian-free sequence training over networks trained on speaker-adapted features only.
TL;DR: In this article, the authors report the results of acoustic analyses performed on the conversational and clear speech, and show that speaking clearly cannot be regarded as equivalent to the application of high-frequency emphasis.
Abstract: The first paper of this series (Picheny, Durlach, & Braida, 1985) presented evidence that there are substantial intelligibility differences for hearing-impaired listeners between nonsense sentences spoken in a conversational manner and spoken with the effort to produce clear speech. In this paper, we report the results of acoustic analyses performed on the conversational and clear speech. Among these results are the following. First, speaking rate decreases substantially in clear speech. This decrease is achieved both by inserting pauses between words and by lengthening the durations of individual speech sounds. Second, there are differences between the two speaking modes in the numbers and types of phonological phenomena observed. In conversational speech, vowels are modified or reduced, and word-final stop bursts are often not released. In clear speech, vowels are modified to a lesser extent, and stop bursts, as well as essentially all word-final consonants, are released. Third, the RMS intensities for obstruent sounds, particularly stop consonants, is greater in clear speech than in conversational speech. Finally, changes in the long-term spectrum are small. Thus, speaking clearly cannot be regarded as equivalent to the application of high-frequency emphasis.
TL;DR: The authors found that the average intelligibility difference between clear and conversational speech averaged 17 percentage points across talker was found to be independent of the listener, level, and frequency-gain characteristic.
Abstract: This paper is concerned with variations in the intelligibility of speech produced for hearing-impaired listeners under two conditions. Estimates were made of the magnitude of the intelligibility differences between attempts to speak clearly and attempts to speak conversationally. Five listeners with sensorineural hearing losses were tested on groups of nonsense sentences spoken clearly and conversationally by three male talkers as a function of level and frequency-gain characteristic. The average intelligibility difference between clear and conversational speech averaged across talker was found to be 17 percentage points. To a first approximation, this difference was independent of the listener, level, and frequency-gain characteristic. Analysis of segmental-level errors was only possible for two listeners and indicated that improvements in intelligibility occurred across all phoneme classes.
•23 Oct 1995
TL;DR: In this paper, a method of automatically aligning a written transcript with speech in video and audio clips is presented. But it does not address the problem of automatic alignment of the transcript with the original transcript.
Abstract: A method of automatically aligning a written transcript with speech in video and audio clips. The disclosed technique involves as a basic component an automatic speech recognizer. The automatic speech recognizer decodes speech (recorded on a tape) and produces a file with a decoded text. This decoded text is then matched with the original written transcript via identification of similar words or clusters of words. The results of this matching is an alignment of the speech with the original transcript. The method can be used (a) to create indexing of video clips, (b) for "teleprompting" (i.e. showing the next portion of text when someone is reading from a television screen), or (c) to enhance editing of a text that was dictated to a stenographer or recorded on a tape for its subsequent textual reproduction by a typist.
••20 Aug 2017
TL;DR: In this article, a set of acoustic and language modeling techniques were used to lower the word error rate of a conversational telephone LVCSR system to 5.5%/10.3% on the Switchboard/CallHome subsets of the Hub5 2000 evaluation.
Abstract: One of the most difficult speech recognition tasks is accurate recognition of human to human communication. Advances in deep learning over the last few years have produced major speech recognition improvements on the representative Switchboard conversational corpus. Word error rates that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This then raises two issues - what IS human performance, and how far down can we still drive speech recognition error rates? A recent paper by Microsoft suggests that we have already achieved human performance. In trying to verify this statement, we performed an independent set of human performance measurements on two conversational tasks and found that human performance may be considerably better than what was earlier reported, giving the community a significantly harder goal to achieve. We also report on our own efforts in this area, presenting a set of acoustic and language modeling techniques that lowered the word error rate of our own English conversational telephone LVCSR system to the level of 5.5%/10.3% on the Switchboard/CallHome subsets of the Hub5 2000 evaluation, which - at least at the writing of this paper - is a new performance milestone (albeit not at what we measure to be human performance!). On the acoustic side, we use a score fusion of three models: one LSTM with multiple feature inputs, a second LSTM trained with speaker-adversarial multi-task learning and a third residual net (ResNet) with 25 convolutional layers and time-dilated convolutions. On the language modeling side, we use word and character LSTMs and convolutional WaveNet-style language models.
18 Nov 2016
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
••01 Feb 1989
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Abstract: This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. >
TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
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.
01 Jan 2000
TL;DR: This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora, to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation.
Abstract: From the Publisher: This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.
TL;DR: In this article, the authors proposed a simple and effective approach for spatio-temporal feature learning using deep 3D convolutional networks (3D ConvNets) trained on a large scale supervised video dataset.
Abstract: We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold: 1) 3D ConvNets are more suitable for spatiotemporal feature learning compared to 2D ConvNets; 2) A homogeneous architecture with small 3x3x3 convolution kernels in all layers is among the best performing architectures for 3D ConvNets; and 3) Our learned features, namely C3D (Convolutional 3D), with a simple linear classifier outperform state-of-the-art methods on 4 different benchmarks and are comparable with current best methods on the other 2 benchmarks. In addition, the features are compact: achieving 52.8% accuracy on UCF101 dataset with only 10 dimensions and also very efficient to compute due to the fast inference of ConvNets. Finally, they are conceptually very simple and easy to train and use.