Describes a complete system for the recognition of off-line handwriting. Preprocessing techniques are described, including segmentation and normalization of word images to give invariance to scale, slant, slope and stroke thickness. Representation of the image is discussed and the skeleton and stroke features used are described. A recurrent neural network is used to estimate probabilities for the characters represented in the skeleton. The operation of the hidden Markov model that calculates the best word in the lexicon is also described. Issues of vocabulary choice, rejection, and out-of-vocabulary word recognition are discussed.

Presents two techniques for language model adaptation. The first is based on the use of mixtures of language models: the training text is partitioned according to topic, a language model is constructed for each component and, at recognition time, appropriate weightings are assigned to each component to model the observed style of language. The second technique is based on augmenting the standard trigram model with a cache component in which the words' recurrence probabilities decay exponentially over time. Both techniques yield a significant reduction in perplexity over the baseline trigram language model when faced with a multi-domain test text, the mixture-based model giving a 24% reduction and the cache-based model giving a 14% reduction. The two techniques attack the problem of adaptation at different scales, and as a result can be used in parallel to give a total perplexity reduction of 30%.

This paper describes a new algorithm to enhance and recognise noisy speech when only the noisy signal is available. The system uses autoregressive hidden Markov models (HMMs) to model the clean speech and noise and combines these to form a model for the noisy speech. The probability framework developed is then used to reestimate the noise models from the corrupted speech waveform and the process is repeated. Enhancement is performed using the Wiener filters formed from the final clean speech models and noise estimates. Results are presented for additive stationary Gaussian and coloured noise.

In this paper we investigate a number of ensemble methods for improving the performance of connectionist acoustic models for large vocabulary continuous speech recognition. We discuss boosting, a data selection technique which results in an ensemble of models, and mixtures-ofexperts. These techniques have been applied to multilayer perceptron acoustic models used to build a hybrid connectionist-HMM speech recognition system. We present results on a number of ARPA benchmark tasks, and show that the ensemble methods lead to considerable improvements in recognition accuracy. 1. INTRODUCTION When developing a classification or prediction system it is common practice to train a number of different models, and to retain the model which exhibits the best performance on a cross-validation data set. However, reports in the statistics and neural network literature suggest that improved performance can be achieved by combining the estimates of all the available models [1, 2, 3, 4]. Systems that...

A new method to improve the accuracy of Autoregressive Hidden Markov Model (AR-HMM) based recognition systems is proposed. The technique uses the bilinear transform to warp the frequency scale of the observation vectors, hence it uses a better perceptual measure to compare the observation vectors to the trained models. Results presented for the E-set letters from the ISOLET database and the first speaker dependent task of the Resource Management (RM) database show that this technique improves recognition accuracy considerably. However, in the case of the RM system, the recognition results still fall short of those obtained from a similar melfrequency cepstral (MFCC) based system without delta parameters. Reasons for the inferior performance of the AR-HMM system are proposed and future research directions are suggested. The models built for the RM task are incorporated into an existing enhancement algorithm to form a large vocabulary speaker dependent enhancement system. Preliminary res...

This paper presents a real-time speech recognition system used to transcribe broadcast radio speech. The system is based on ABBOT, the hybrid connectionist-HMM large vocabulary continuous speech recognition system developed at the Cambridge University Engineering Department. Developments designed to make the system more robust to acoustic variability and to improve performance when decoding spontaneous speech are described. Modifications necessary to increase the speed of the system so that it operates in real-time are also described. Recognition results and latency figures are presented for speech collected from broadcast news segments on BBC Radio 4.

ABBOT is the hybrid connectionist-hidden Markov model (HMM) large-vocabulary continuous speech recognition (CSR) system developed at Cambridge University. This system uses a recurrent network to estimate the acoustic observation probabilities within an HMM framework. A major advantage of this approach is that good performance is achieved using context-independent acoustic models and requiring many fewer parameters than comparable HMM systems. This paper presents substantial performance improvements gained from new approaches to connectionist model combination and phone-duration modeling. Additional capability has also been achieved by extending the decoder to handle larger vocabulary tasks (20000 words and greater) with a trigram language model. This paper describes the modifications to the system and experimental results are reported for various test and development sets from the November 1992, 1993, and 1994 ARPA evaluations of spoken language systems.

Conventional speaker independent speech recognition systems are trained using data from many different speakers. Inter-speaker variability is a major problem because parametric representations of speech are highly speaker dependent. This paper describes a technique which allows speaker dependent parameters to be considered when building a speaker independent speech recognition system. The technique is based on utterance clustering, where subsets of the training data are formed and the variability within each subset minimized. Cluster dependent connectionist models are then used to estimate phone probabilities as part of a hybrid connectionist hidden Markov model based large vocabulary talker independent speech recognition system. The system has been evaluated on the ARPA Wall Street Journal continuous speech recognition task. 1. INTRODUCTION Speaker dependent speech recognition systems are generated using training utterances from a single speaker, resulting in a system tuned to a spec...

In this paper we consider speech coding as a problem of speech modelling. In particular, prediction of parameterised speech over short time segments is performed using the Hierarchical Mixture of Experts (HME) (Jordan & Jacobs 1994). The HME gives two ad(cid:173) vantages over traditional non-linear function approximators such as the Multi-Layer Percept ron (MLP); a statistical understand(cid:173) ing of the operation of the predictor and provision of information about the performance of the predictor in the form of likelihood information and local error bars. These two issues are examined on both toy and real world problems of regression and time series prediction. In the speech coding context, we extend the principle of combining local predictions via the HME to a Vector Quantiza(cid:173) tion scheme in which fixed local codebooks are combined on-line for each observation.

This paper presents an application of recurrent networks for phone probability estimation in large vocabulary speech recognition. The need for efficient exploitation of context information is discussed; a role for which the recurrent net appears suitable. An overview of early developments of recurrent nets for phone recognition is given along with the more recent improvements that include their integration with Markov models. Recognition results are presented for the DARPA TIMIT and Resource Management tasks, and it is concluded that recurrent nets are competitive with traditional means for performing phone probability estimation.

Four types of neural networks which have previously been established for speech recognition and tested on a small, seven-speaker, 100-sentence database are applied to the TIMIT database. The networks are a recurrent network phoneme recognizer, a modified Kanerva model morph recognizer, a compositional representation phoneme-to-word recognizer, and a modified Kanerva model morph-to-word recognizer. The major result is for the recurrent net, giving a phoneme recognition accuracy of 57% from the si and sx sentences. The Kanerva morph recognizer achieves 66.2% accuracy for a small subset of the sa and sx sentences. The results for the word recognizers are incomplete.>