. In this paper, we discuss a new automatic speech recognition (ASR) approach based on independent processing and recombination of several feature streams. In this framework, it is assumed that the speech signal is represented in terms of multiple input streams, each input stream representing a different characteristic of the signal. If the streams are entirely synchronous, they may be accommodated simply (as they usually are in state-of-the-art systems). However, as discussed in the paper, it may be required to permit some degree of asynchrony between streams. This paper introduces the basic framework of a statistical structure that can accommodate multiple (asynchronous) observation streams (possibly exhibiting different frame rates). This approach will then be applied to the particular case of multi-band speech recognition and will be shown to yield significantly better noise robustness. 2 IDIAP--RR 96-07 1 Introduction In current automatic speech recognition (ASR) systems, the a...

A method for incorporating context-dependent phone classes in a connectionist-HMM hybrid speech recognition system is introduced. A modular approach is adopted, where single-layer networks discriminate between different context classes given the phone class and the acoustic data. The context networks are combined with a context-independent (CI) network to generate context-dependent (CD) phone probability estimates. Experiments show an average reduction in word error rate of 16% and 13% from the CI system on ARPA 5,000 word and SQALE 20,000 word tasks respectively. Due to improved modelling, the decoding speed of the CD system is more than twice as fast as the CI system. INTRODUCTION The abbot hybrid connectionist-HMM system performed competitively with many conventional hidden Markov model (HMM) systems in the 1994 ARPA evaluations of speech recognition systems (Hochberg, Cook, Renals, Robinson & Schechtman 1995). This hybrid framework is attractive because it is compact, having far f...

. Initially introduced in the late 1960s and early 1970s, dynamic programming algorithms have become increasingly popular in automatic speech recognition. There are two reasons why this has occurred: First, the dynamic programming strategy can be combined with avery e#cient and practical pruning strategy so that very large search spaces can be handled. Second, the dynamic programming strategy has turned out to be extremely #exible in adapting to new requirements. Examples of such requirements are the lexical tree organization of the pronunciation lexicon and the generation of a word graph instead of the single best sentence. In this paper, we attempt to systematically review the use of dynamic programming search strategies for small#vocabulary and large#vocabulary continuous speech recognition. The following methods are described in detail: search using a linear lexicon, search using a lexical tree, language-model look-ahead and word graph generation. 1 Introduction Search strategie...

This paper describes connectionist techniques for recognition of Broadcast News. The fundamental difference between connectionist systems and more conventional mixture-of-Gaussian systems is that connectionist models directly estimate posterior probabilities as opposed to likelihoods. Access to posterior probabilities has enabled us to develop a number of novel approaches to confidence estimation, pronunciation modelling and search. In addition we have investigated a new feature extraction technique based on the modulation-filtered spectrogram (MSG), and methods for combining multiple information sources. We have incorporated all of these techniques into a system for the transcription

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In this paper, we present an efficient look-ahead technique which incorporates the language model knowledge at the earliest possible stage during the search process. This so-called language model look-ahead is built into the time synchronous beam search algorithm using a tree-organized pronunciation lexicon for a bigram language model. The language model look-ahead technique exploits the full knowledge of the bigram language model by distributing the language model probabilities over the nodes of the lexical tree for each predecessor word. We present a method for handling the resulting memory requirements. The recognition experiments performed on the 20 000-word North American Business task (Nov.'96) demonstrate that in comparison with the unigram look-ahead a reduction by a factor of 5 in the acoustic search effort can be achieved without loss in recognition accuracy.

this paper, we present two efficient look-ahead pruning techniques in beam search for large vocabulary continuous speech recognition. Both techniques, the language model look-ahead and the phoneme look-ahead, are incorporated into the word conditioned search algorithm using a bigram language model and a lexical prefix tree [5]. The paper present the following novel contributions: ffl We describe a method for language model (LM) look-ahead pruning which is similar to [1, 9]. We show special techniques to reduce the memory and computational requirements. These techniques are based on a compressed LM look-ahead tree. To compute the LM look-ahead tree probabilites in an efficient way, we present a backward dynamic programming scheme

Wehave trained and tested a number of large neural networks for the purpose of emission probability estimation in large vocabulary continuous speech recognition. In particular, the problem under test is the DARPA Broadcast News task. Our goal here was to determine the relationship between training time, word error rate, size of the training set, and size of the neural network. In all cases, the network architecture was quite simple, comprising a single large hidden layer with an input window consisting of feature vectors from 9 frames around the current time, with a single output for each of 54 phonetic categories. Thus far, simultaneous increases to the size of the training set and the neural network improve performance; in other words, more data helps, as does the training of more parameters. We continue to be surprised that such a simple system works as well as it does for complex tasks. Given a limitation in training time, however, there appears to be an optimal ratio of training p...

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 (20,000 words and greater) with a trigram language model. This paper describes the recent 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. 1. INTRODUCTION This p...

visualization of the problem to model human speech. A large number of example sequences of observation vectors (shown connected as continuous trajectories) depending on a given sequence of class labels, with each class representing for example a phoneme (here the name Keiko with given durations). In this synthetic example, the one-dimensional target data would be represented poorly by a uni-modal Gaussian distribution with a constant variance (which corresponds to using the squared-error objective function), which would average the two separate branches, indicated by the fat lines as the mean and constant variance of the single Gaussian. Compare this figure with Figure 3.10, Figure 3.11 and Figure 3.12 to see a subsequent improvement of the model.