In this paper a framework for maximum a posteriori (MAP) estimation of hidden Markov models (HMM) is presented. Three key issues of MAP estimation, namely the choice of prior distribution family, the specification of the parameters of prior densities and the evaluation of the MAP estimates, are addressed. Using HMMs with Gaussian mixture state observation densities as an example, it is assumed that the prior densities for the HMM parameters can be adequately represented as a product of Dirichlet and normal-Wishart densities. The classical maximum likelihood estimation algorithms, namely the forward-backward algorithm and the segmental k-means algorithm, are expanded and MAP estimation formulas are developed. Prior density estimation issues are discussed for two classes of applications: parameter smoothing and model adaptation, and some experimental results are given illustrating the practical interest of this approach. Because of its adaptive nature, Bayesian learning is shown to serve as a unified approach for a wide range of speech recognition applications

In large-vocabulary speech recognition, the decoder often encounters triphones that are not covered in the training data. These unseen triphones are usually represented by corresponding diphones or context independent monophones. We propose to use decision-tree based senones to generate needed senonic baseforms for unseen triphones. A decision tree is built for each individual Markov state of each phone, and the leaves of the trees constitute the senone codebook. To find the senone a Markov state of any triphone is associated with, we traverse the corresponding tree until we reach a leaf node, where a senone is represented. We used the DARPA 5,000-word speaker-independent Wall Street Journal dictation task to evaluate the proposed method. The word error rate was reduced by 11% when unseen triphones were modeled by the decision-tree based senones. When there were at least 5 unseen triphones in each test utterance, the error rate could be reduced by more than 20%. This research was spons...

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.1. Approaches to Overcoming Environmental Variability . . . . . . . . . . . . . . 6 1.1.1. Re-Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1.2. Multi-Style Training . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.1.3. Environmental Compensation Using Dynamic Adaptation . . . . . . . . . . 8 1.2. Towards Environment-Independent Recognition . . . . . . . . . . . . . . . . 8 1.2.1. Sources of Environmental Variability . . . . . . . . . . . . . . . . . . 9 1.2.2. Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . 9 1.3. Dissertation Outline . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Chapter 2 Overview of Environmental Robustness in Speech Recognition . . . . . . 12 2.1. Sources of Degradation...

Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine

Decision tree based state tying uses so-called phonetic questions to assign triphone states to reasonable acoustic models. These phonetic questions are in fact phonetic categories such as vowels, plosives or fricatives. The assumption behind this is that context phonemes which belong to the same phonetic class have a similar influence on the pronunciation of a phoneme. For a new phoneme set, which has to be used e.g. when switching to a different corpus, a phonetic expert is needed to define proper phonetic questions. In this paper a new method is presented which automatically defines good phonetic questions for a phoneme set. This method uses the intermediate clusters from a phoneme clustering algorithm which are reduced to an appropriate number afterwards. Recognition results on the Wall Street Journal data for within-word and acrossword phoneme models show competitive performance of the automatically generated questions with our best handcrafted question set.

In this paper we propose to use a decision tree based on graphemic acoustic sub-word units together with phonetic questions. We also show that automatic question generation can be used to completely eliminate any manual effort.

this paper several modifications of two methods for parameter reduction of Hidden Markov Models by state tying are described. The two methods represent a data driven clustering triphone states with a bottom up algorithm [3, 9], and a top down method growing decision trees for triphone states [2, 10]. We investigate several aspects of state tying as the possible reduction of the word error rate by state tying, the consequences of different distance measures for the data driven approach and modifications of the original decision tree approach such as node merging. The tests were performed on the test corpora for the 5 000 word vocabulary of the WSJ November 92 task and on the evaluation corpora for the 3 000 word VERBMOBIL '95 task. The word error rate by state tying was reduced by 14% for the WSJ task and by 5% for the VERBMOBIL task

JANUS [17] was designed for the translation of spontaneous human-to-human speech. Before the 1994 CSR evaluation, JANUS was run with vocabularies of up to 2500 words. JANUS was also tested on the Conference Registration and the Resource Management tasks. The best error rate on the '89 Resource Management evaluation set was 5.9%. At the June 1994 Verbmobil speech component evaluation [1], JANUS scored best among eight participants on the German appointment scheduling task, a task of spontaneous human to human dialogs. In this paper we give a detailed description of the recognition engine of JANUS, focusing on the acoustic modeling and our first run with the WSJ task. 1. ACOUSTIC MODELING IN JANUS 1.1 PREPROCESSING For the 1994 CSR evaluation we computed 16 mel scale spectral coefficients from an FFT with a window size of 256 sample points and a window shift (frame rate) of 10 ms. 16 mel spectral coefficients, 16 delta coefficients, and 16 delta-delta coefficients were used to build a 4...

For the past two decades, research in speech recognition has been intensively carried out worldwide, spurred on by advances in signal processing, algorithms, architectures, and hardware. Speech recognition systems have been developed for a wide variety of applications, ranging from small vocabulary keyword recognition over dial-up telephone lines, to medium size vocabulary voice interactive command and control systems on personal computers, to large vocabulary speech dictation, spontaneous speech understanding, and limited-domain speech translation. In this paper we review some of the key advances in several areas of automatic speech recognition. We also illustrate, by examples, how these key advances can be used for continuous speech recognition of Mandarin. Finally we elaborate the requirements in designing successful real-world applications and address technical challenges that need to be harnessed in order to reach the ultimate goal of providing an easy-to-use, natural, and flexible voice interface between people and machines.