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. >

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A tutorial on hidden Markov models and selected applications in speech recognition

The use of hidden Markov models for speech recognition has become predominant in the last several years, as evidenced by the number of published papers and talks at major speech conferences. The reasons this method has become so popular are the inherent statistical (mathematically precise) framework; the ease and availability of training algorithms for cstimating the parameters of the models from finite training sets of speech data; the flexibility of the resulting recognition system in which one can easily change the size, type, or architecture of the models to suit particular words, sounds, and so forth; and the ease of implementation of the overall recognition system. In this expository article, we address the role of statistical methods in this powerful technology as applied to speech recognition and discuss a range of theoretical and practical issues that are as yet unsolved in terms of their importance and their effect on performance for different system implementations.

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Hidden Markov models for speech recognition

From the Publisher:
Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous speech recognition systems based on Hidden Markov Models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e., HMM emission probability estimation and feature extraction. The book describes a successful five year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical system. Using standard databases and comparing with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods. Connectionist Speech Recognition: A Hybrid Approach is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. This book is also suitable as a text for advanced courses on neural networks or speech processing.

Connectionist Speech Recognition: A Hybrid Approach

The environmental robustness of DNN-based acoustic models can be significantly improved by using multi-condition training data. However, as data collection is a costly proposition, simulation of the desired conditions is a frequently adopted strategy. In this paper we detail a data augmentation approach for far-field ASR. We examine the impact of using simulated room impulse responses (RIRs), as real RIRs can be difficult to acquire, and also the effect of adding point-source noises. We find that the performance gap between using simulated and real RIRs can be eliminated when point-source noises are added. Further we show that the trained acoustic models not only perform well in the distant-talking scenario but also provide better results in the close-talking scenario. We evaluate our approach on several LVCSR tasks which can adequately represent both scenarios.

A study on data augmentation of reverberant speech for robust speech recognition

https://www.ee.columbia.edu/~dpwe/classes/e6820-2005-01/papers/Lipp97-hummach.pdf

Speech recognition by machines and humans

A new training procedure called multi-style training has been developed to improve performance when a recognizer is used under stress or in high noise but cannot be trained in these conditions Instead of speaking normally during training, talkers use different, easily produced, talking styles This technique was tested using a speech data base that included stress speech produced during a workload task and when intense noise was presented through earphones A continuous-distribution talker-dependent Hidden Markov Model (HMM) recognizer was trained both normally (5 normally spoken tokens) and with multi-style training (one token each from normal, fast, clear, loud, and question-pitch talking styles) The average error rate under stress and normal conditions fell by more than a factor of two with multi-style training and the average error rate under conditions sampled during training fell by a factor of four

Multi-style training for robust isolated-word speech recognition

Most speech recognizers are sensitive to the speech style and the speaker's environment. This system extends an earlier robust continuous observation HMM IWR system to continuous speech using the DARPA-robust (multi-condition with a pilot's facemask) database. Performance on a 207 word, perplexity 14 task is 0.9% word error rate under office conditions and 2.5% (best speaker) and 5% (4 speaker average) for the normal test condition of the database. >

Speaker stress-resistant continuous speech recognition

This paper describes a two-stage isolated word speech recognition system that uses a Hidden Markov Model (HMM) recognizer in the first stage and a discriminant analysis system in the second stage. During recognition, when the first-stage recognizer is unable to clearly differentiate between acoustically similar words such as "go" and "no" the second-stage discriminator is used. The second-stage system focuses on those parts of the unknown token which are most effective at discriminating the confused words. The system was tested on a 35 word, 10,710 token stress speech isolated word data base created at Lincoln Laboratory. Adding the second-stage discriminating system produced the best results to date on this data base, reducing the overall error rate by more than a factor of two.

Two-stage discriminant analysis for improved isolated-word recognition

The authors describe an HMM-based isolated-word recognition system that dynamically adapts word model parameters to new speakers and to stress-induced speech variations. During recognition all input tokens presented to the system can be used to augment the current word model parameters. New tokens can be weighted so that adaptation simply increases the size of the training set, or tracks systematic changes by exponentially weighting all previously seen data. This system was tested on the 35-word 10710 token Lincoln stressed speech data base. Speaker adaptation experiments produced error rates equivalent to speaker-trained systems after the presentation of only a single new token per vocabulary word. Stress condition adaptation experiments produced results comparable to multistyle-trained systems after the presentation of several new tokens per vocabulary word. >

Dynamic adaptation of Hidden Markov models for robust isolated-word speech recognition

One limitation of hidden Markov model (HMM) recognizers is that subword models are not learned but must be prespecified before training. This can lead to excessive computation during recognition and/or poor discrimination between similar sounding words. A training procedure called discriminant clustering is presented that creates subword models automatically. Node sequences from whole-word models are merged using statistical clustering techniques. This procedure reduced the computation required during recognition for a 35-word vocabulary by roughly one-third while maintaining a low error rate. It was also found that five iterations of the forward-backward algorithm are sufficient and that adding nodes to HMM word models improves performance until the minimum word transition time becomes excessive. >

E.A. Martin

Papers

Multi-style training for robust isolated-word speech recognition

Speaker stress-resistant continuous speech recognition

Two-stage discriminant analysis for improved isolated-word recognition

Dynamic adaptation of Hidden Markov models for robust isolated-word speech recognition

Discriminant clustering using an HMM isolated-word recognizer