This paper describes the development of the 2003 CU-HTK large vocabulary speech recognition system for Conversational Telephone Speech (CTS). The system was designed based on a multipass, multi-branch structure where the output of all branches is combined using system combination. A number of advanced modelling techniques such as Speaker Adaptive Training, Heteroscedastic Linear Discriminant Analysis, Minimum Phone Error estimation and specially constructed Single Pronunciation dictionaries were employed. The effectiveness of each of these techniques and their potential contribution to the result of system combination was evaluated in the framework of a state-of-the-art LVCSR system with sophisticated adaptation. The final 2003 CU-HTK CTS system constructed from some of these models is described and its performance on the DARPA/NIST 2003 Rich Transcription (RT-03) evaluation test set is discussed.

This paper describes recent advances in the CU-HTK Broadcast News English (BN-E) transcription system and its performance in the DARPA/NIST Rich Transcription 2003 Speech-to-Text (RT03) evaluation. Heteroscedastic linear discriminant analysis (HLDA) and discriminative training, which were previously developed in the context of the recognition of conversational telephone speech, have been successfully applied to the BN-E task for the first time. A number of new features have also been added. These include gender-dependent (GD) discriminative training; and modified discriminative training using lattice re-generation and combination. On the 2003 evaluation set the system gave an overall word error rate of 10.7% in less than 10 times real time (10RT).

This paper describes the development of fast (less than 10 times real-time) large vocabulary continuous speech recognition (LVCSR) systems based on technology developed for unlimited runtime systems assembled for participation in recent DARPA/NIST LVCSR evaluations. A general system structure for 10 times real-time systems is proposed and two specific systems that have been built for Broadcast News (BN) and Conversational Telephone Speech (CTS) recognition are described. The systems were evaluated in the DARPA/NIST April 2003 Rich Transcription evaluation. Results are reported and contrasted with unlimited runtime systems and previous fast systems.

Abstract—Large-vocabulary speech recognition systems are often built using found data, such as broadcast news. In contrast to carefully collected data, found data normally contains multiple acoustic conditions, such as speaker or environmental noise. Adaptive training is a powerful approach to build systems on such data. Here, transforms are used to represent the different acoustic conditions, and then a canonical model is trained given this set of transforms. This paper describes a Bayesian framework for adaptive training and inference. This framework addresses some limitations of standard maximum-likelihood approaches. In contrast to the standard approach, the adaptively trained system can be directly used in unsupervised inference, rather than having to rely on initial hypotheses being present. In addition, for limited adaptation data, robust recognition performance can be obtained. The limited data problem often occurs in testing as there is no control over the amount of the adaptation data available. In contrast, for adaptive training, it is possible to control the system complexity to reflect the available data. Thus, the standard point estimates may be used. As the integral associated with Bayesian adaptive inference is intractable, various marginalization approximations are described, including a variational Bayes approximation. Both batch and incremental modes of adaptive inference are discussed. These approaches are applied to adaptive training of maximum-likelihood linear regression and evaluated on a large-vocabulary speech recognition task. Bayesian adaptive inference is shown to significantly outperform standard approaches. Index Terms—Adaptive training, Bayesian adaptation, Bayesian inference, incremental, variational Bayes.

The most commonly used approaches to speaker adaptation are based on linear transforms, as these can be robustly estimated using limited adaptation data. Although significant gains can be obtained using discriminative criteria for training acoustic models, maximum likelihood (ML) estimated transforms are used for unsupervised adaptation. This is because discriminatively trained transforms are highly sensitive to errors in the adaptation hypothesis. This paper describes a new framework for estimating transforms that are discriminative in nature, but are less sensitive to this hypothesis issue. A discriminative, speaker-independent, mapping transformation is estimated during training. This transform is obtained after a speaker-specific ML-estimated transform has been applied. During recognition an ML speaker-specific transform is found and the speaker-independent discriminative mapping transform then applied. This allows a transform which is discriminative in nature to be indirectly estimated, whilst only requiring an ML speaker-specific transform to be found during recognition. The scheme is evaluated on an English conversational telephone speech task, where it significantly outperforms both standard ML and discriminatively trained transforms.

Summary In recent years, there has been a trend towards training large vocabulary continuous speech recognition (LVCSR) systems on a large amount of found data. Found data is recorded from spontaneous speech without careful control of the recording acoustic conditions, for example, conversational telephone speech. Hence, it typically has greater variability in terms of speaker and acoustic conditions than specially collected data. Thus, in addition to the desired speech variability required to discriminate between words, it also includes various non-speech variabil-ities, for example, the change of speakers or acoustic environments. The standard approach to handle this type of data is to train hidden Markov models (HMMs) on the whole data set as if all data comes from a single acoustic condition. This is referred to as multi-style training, for exam-ple speaker-independent training. Effectively, the non-speech variabilities are ignored. Though good performance has been obtained with multi-style systems, these systems account for all variabilities. Improvement may be obtained if the two types of variabilities in the found data are modelled separately. Adaptive training has been proposed for this purpose. In contrast to multi-style training, a set of transforms is used to represent the non-speech variabilities. A canonical

Abstract — Broadcast News (BN) transcription has been a challenging research area for many years. In the last couple of years the availability of large amounts of roughly transcribed acoustic training data and advanced model training techniques has offered the opportunity to greatly reduce the error rate on this task. This paper describes the design and performance of BN transcription systems which make use of these developments. First the effects of using lightly-supervised training data and advanced acoustic modelling techniques are discussed. The design of a real-time broadcast news recognition system is then detailed using these new models. As system combination has been found to yield large gains in performance, a range of frameworks that allow multiple recognition outputs to be combined are next described. These include the use of multiple types of acoustic models and multiple segmentations. As a contrast a system developed by multiple sites allowing cross-site combination, the “SuperEARS ” system, is also described. The various models and recognition configurations are evaluated using several recent BN development and evaluation test sets. These new BN transcription systems can give gains of over 25 % relative to the CU-HTK 2003 BN system.

Abstract—The most commonly used approaches to speaker adaptation are based on linear transforms, as these can be robustly estimated using limited adaptation data. Although significant gains can be obtained using discriminative criteria for training acoustic models, maximum-likelihood (ML) estimated transforms are still used for unsupervised adaptation. This is because discriminatively trained transforms are highly sensitive to errors in the adaptation supervision hypothesis. This paper describes a new framework for estimating transforms that are discriminative in nature, but are less sensitive to this hypothesis issue. A speaker-independent discriminative mapping transformation (DMT) is estimated during training. This transform is obtained after a speaker-specific ML-estimated transform of each training speaker has been applied. During recognition an ML speaker-specific transform is found for each test-set speaker and the speaker-independent DMT then applied. This allows a transform which is discriminative in nature to be indirectly estimated, while only requiring an ML speaker-specific transform to be found during recognition. The DMT technique is evaluated on an English conversational telephone speech task. Experiments showed that using DMT in unsupervised adaptation led to significant gains over both standard ML and discriminatively trained transforms. Index Terms—Criterion mapping function, discriminative mapping transform, discriminative training, unsupervised adaptation. I.

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This paper addresses the use of discriminative training criteria for Speaker Adaptive Training (SAT), where both the transform generation and model parameter estimation are estimated using the Minimum Phone Error (MPE) criterion. In a similar fashion to the use of I-smoothing for standard MPE training, a smoothing technique is introduced to avoid over-training when optimizing MPEbased feature-space transforms. Experiments on a Conversational Telephone Speech (CTS) transcription task demonstrate that MPEbased SAT models can reduce the word error rate over non-SAT MPE models by 1.0% absolute, after lattice-based MLLR adaptation. Moreover, a simplified implementation of MPE-SAT with the use of constrained MLLR, in place of MPE-estimated transforms, is also discussed.