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Page 165 State of the Art in Continuous Speech Recognition John Makhoul and Richard Schwartz SUMMARY In the past decade, tremendous advances in the state of the art of automatic speech recognition by machine have taken place. A reduction in the word error rate by more than a factor of 5 and an increase in recognition speeds by several orders of magnitude (brought about by a combination of faster recognition search algorithms and more powerful computers), have combined to make high-accuracy, speaker-independent, continuous speech recognition for large vocabularies possible in real time, on off-the-shelf workstations, without the aid of special hardware. These advances promise to make speech recognition technology readily available to the general public. This paper focuses on the speech recognition advances made through better speech modeling techniques, chiefly through more accurate mathematical modeling of speech sounds. INTRODUCTION More and more, speech recognition technology is making its way from the laboratory to real-world applications. Recently, a qualitative change in the state of the art has emerged that promises to bring speech recognition capabilities within the reach of anyone with access to a workstation. High-accuracy, real-time, speaker-independent,
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Page 166 continuous speech recognition for medium-sized vocabularies (a few thousand words) is now possible in software on off-the-shelf workstations. Users will be able to tailor recognition capabilities to their own applications. Such software-based, real-time solutions usher in a whole new era in the development and utility of speech recognition technology. As is often the case in technology, a paradigm shift occurs when several developments converge to make a new capability possible. In the case of continuous speech recognition, the following advances have converged to make the new technology possible: • higher-accuracy continuous speech recognition, based on better speech modeling techniques; • better recognition search strategies that reduce the time needed for high-accuracy recognition; and • increased power of audio-capable, off-the-shelf workstations. The paradigm shift is taking place in the way we view and use speech recognition. Rather than being mostly a laboratory endeavor, speech recognition is fast becoming a technology that is pervasive and will have a profound influence on the way humans communicate with machines and with each other. This paper focuses on speech modeling advances in continuous speech recognition, with an exposition of hidden Markov models (HMMs), the mathematical backbone behind these advances. While knowledge of properties of the speech signal and of speech perception have always played a role, recent improvements have relied largely on solid mathematical and probabilistic modeling methods, especially the use of HMMs for modeling speech sounds. These methods are capable of modeling time and spectral variability simultaneously, and the model parameters can be estimated automatically from given training speech data. The traditional processes of segmentation and labeling of speech sounds are now merged into a single probabilistic process that can optimize recognition accuracy. This paper describes the speech recognition process and provides typical recognition accuracy figures obtained in laboratory tests as a function of vocabulary, speaker dependence, grammar complexity, and the amount of speech used in training the system. As a result of modeling advances, recognition error rates have dropped several fold. Important to these improvements have been the availability of common speech corpora for training and testing purposes and the adoption of standard testing procedures. This paper also reviews more recent research directions, including the use of segmental models and artificial neural networks in
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Page 167 improving the performance of HMM systems. The capabilities of neural networks to model highly nonlinear functions can be used to develop new features from the speech signal, and their ability to model posterior probabilities can be used to improve recognition accuracy. We will argue that future advances in speech recognition must continue to rely on finding better ways to incorporate our speech knowledge into advanced mathematical models, with an emphasis on methods that are robust to speaker variability, noise, and other acoustic distortions. THE SPEECH RECOGNITION PROBLEM Automatic speech recognition can be viewed as a mapping from a continuous-time signal, the speech signal, to a sequence of discrete entities, for example, phonemes (or speech sounds), words, and sentences. The major obstacle to high-accuracy recognition is the large variability in the speech signal characteristics. This variability has three main components: linguistic variability, speaker variability, and channel variability. Linguistic variability includes the effects of phonetics, phonology, syntax, semantics, and discourse on the speech signal. Speaker variability includes intra- and interspeaker variability, including the effects of coarticulation, that is, the effects of neighboring sounds on the acoustic realization of a particular phoneme, due to continuity and motion constraints on the human articulatory apparatus. Channel variability includes the effects of background noise and the transmission channel (e.g., microphone, telephone, reverberation). All these variabilities tend to shroud the intended message with layers of uncertainty, which must be unraveled by the recognition process. General Synthesis/Recognition Process We view the recognition process as one component of a general synthesis/recognition process, as shown in Figure 1. We assume that the synthesis process consists of three components: a structural model, a statistical variability model, and the synthesis of the speech signal. The input is some underlying event, such as a sequence of words, and the output is the actual speech signal. The structural model comprises many aspects of our knowledge of speech and language, and the statistical variability model accounts for the different variabilities that are encountered. The recognition process begins with analysis of the speech signal into a sequence of feature vectors. This analysis serves to reduce one aspect of signal variability due to changes in
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Page 168 FIGURE 1 General synthesis/recognition process. pitch, etc. Given the sequence of feature vectors, the recognition process reduces to a search over all possible events (word sequences) for that event which has the highest probability given the sequence of feature vectors, based on the structural and statistical variability models used in the synthesis. It is important to note that a significant and important amount of speech knowledge is incorporated in the structural model, including our knowledge of language structure, speech production, and speech perception. Examples of language structure include the fact that continuous speech consists of a concatenation of words and that words are a concatenation of basic speech sounds or phonemes. This knowledge of language structure is quite ancient, being at least 3000 years old. A more recent aspect of language structure that was appreciated in this century is the fact that the acoustic realization of phonemes is heavily dependent on the neighboring context. Our knowledge of speech production, in terms of manner of articulation (e.g., voiced, fricated, nasal) and place of articulation (e.g., velar, palatal, dental, labial), for example, can be used to provide parsimonious groupings of phonetic context. As for speech perception, much is known about sound analysis in the cochlea, for example, that the basilar membrane performs a form of quasi-spectral analysis on a nonlinear frequency scale, and about masking phenomena in time and frequency. All this knowledge can be incorporated beneficially in our modeling of the speech signal for recognition purposes. Units of Speech To gain an appreciation of what modeling is required to perform recognition, we shall use as an example the phrase "grey whales," whose speech signal is shown at the bottom of Figure 2 with the corresponding spectrogram (or voice print) shown immediately above. The spectrogram shows the result of a frequency analysis of the speech,
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Page 169 FIGURE 2 Units of speech. with the dark bands representing resonances of the vocal tract. At the top of Figure 2 are the two words "grey" and "whales," which are the desired output of the recognition system. The first thing to note is that the speech signal and the spectrogram show no separation between the two words ''grey" and "whales" at all; they are in fact connected. This is typical of continuous speech; the words are connected to each other, with no apparent separation. The human perception that a speech utterance is composed of a sequence of discrete words is a purely perceptual phenomenon. The reality is that the words are not separated at all physically. Below the word level in Figure 2 is the phonetic level. Here the words are represented in terms of a phonetic alphabet that tells us what the different sounds in the two words are. In this case the phonetic transcription is given by [g r ey w ey 1 z ]. Again, while the sequence of phonemes is discrete, there is no physical separation
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Page 170 between the different sounds in the speech signal. In fact, it is not clear where one sound ends and the next begins. The dashed vertical lines shown in Figure 2 give a rough segmentation of the speech signal, which shows approximately the correspondences between the phonemes and the speech. Now, the phoneme [ey] occurs once in each of the two words. If we look at the portions of the spectrogram corresponding to the two [ey] phonemes, we notice some similarities between the two parts, but we also note some differences. The differences are mostly due to the fact that the two phonemes are in different contexts: the first [ey] phoneme is preceded by [r] and followed by [w], while the second is preceded by [w] and followed by . These contextual effects are the result of what is known as coarticulation, the fact that the articulation of each sound blends into the articulation of the following sound. In many cases, contextual phonetic effects span several phonemes, but the major effects are caused by the two neighboring phonemes. To account for the fact that the same phoneme has different acoustic realizations, depending on the context, we refer to each specific context as an allophone. Thus, in Figure 2, we have two different allophones of the phoneme [ey], one for each of the two contexts in the two words. In this way, we are able to deal with the phonetic variability that is inherent in coarticulation and that is evident in the spectrogram of Figure 2. To perform the necessary mapping from the continuous speech signal to the discrete phonetic level, we insert a modela finite-state machine in our casefor each of the allophones that are encountered. We note from Figure 2 that the structures of these models are identical; the differences will be in the values given to the various model parameters. Each of these models is a hidden Markov model, which is discussed below. HIDDEN MARKOV MODELS Markov Chains Before we explain what a hidden Markov model is, we remind the reader of what a Markov chain is. A Markov chain consists of a number of states, with transitions among the states. Associated with each transition is a probability and associated with each state is a symbol. Figure 3 shows a three-state Markov chain, with transition probabilities aij. between states i and j. The symbol A is associated with state 1, the symbol B with state 2, and the symbol C with state 3. As one transitions from state 1 to state 2, for example, the symbol B is
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Page 171 produced as output. If the next transition is from state 2 to itself, the symbol B is output again, while if the transition were to state 3, the output would be the symbol C. These symbols are called output symbols because a Markov chain is thought of as a generative model; it outputs symbols as one transitions from one state to another. Note that in a Markov chain the transitioning from one state to another is probabilistic, but the production of the output symbols is deterministic. Now, given a sequence of output symbols that were generated by a Markov chain, one can retrace the corresponding sequence of states completely and unambiguously (provided the output symbol for each state was unique). For example, the sample symbol sequence B A A C B B A C C C A is produced by transitioning into the following sequence of states: 2 11 3 2 2 1 3 3 3 1. Hidden Markov Models A hidden Markov model (HMM) is the same as a Markov chain, except for one important difference: the output symbols in an HMM are probabilistic. Instead of associating a single output symbol per state, in an HMM all symbols are possible at each state, each with its own probability. Thus, associated with each state is a probability distribution of all the output symbols. Furthermore, the number of output symbols can be arbitrary. The different states may then have different probability distributions defined on the set of output symbols. The probabilities associated with states are known as output probabilities. (If instead of having a discrete number of output sym-
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Page 172 FIGURE 4 A three-state hidden Markov model. bols we have a continuously valued vector, it is possible to define a probability density function over all possible values of the random output vector. For the purposes of this exposition, we shall limit our discussion to discrete output symbols.) Figure 4 shows an example of a three-state HMM. It has the same transition probabilities as the Markov chain of Figure 3. What is different is that we associate a probability distribution b,(s) with each state i, defined over the set of output symbols sin this case we have five output symbolsA, B, C, D, and E. Now, when we transition from one state to another, the output symbol is chosen according to the probability distribution corresponding to that state. Compared to a Markov chain, the output sequences generated by an HMM are what is known as doubly stochastic: not only is the transitioning from one state to another stochastic (probabilistic) but so is the output symbol generated at each state. Now, given a sequence of symbols generated by a particular HMM, it is not possible to retrace the sequence of states unambiguously. Every sequence of states of the same length as the sequence of symbols is possible, each with a different probability. Given the sample output sequenceC D A A B E D B A C Cthere is no way for sure to know which sequence of states produced these output symbols. We say that the sequence of states is hidden in that it is hidden from the observer if all one sees is the output sequence, and that is why these models are known as hidden Markov models. Even though it is not possible to determine for sure what se-
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Page 173 quence of states produced a particular sequence of symbols, one might be interested in the sequence of states that has the highest probability of having generated the given sequence. To find such a sequence of states requires a search procedure that, in principle, must examine all possible state sequences and compute their corresponding probabilities. The number of possible state sequences grows exponentially with the length of the sequence. However, because of the Markov nature of an HMM, namely that being in a state is dependent only on the previous state, there is an efficient search procedure called the Viterbi algorithm (Forney, 1973) that can find the sequence of states most likely to have generated the given sequence of symbols, without having to search all possible sequences. This algorithm requires computation that is proportional to the number of states in the model and to the length of the sequence. Phonetic HMMs We now explain how HMMs are used to model phonetic speech events. Figure 5 shows an example of a three-state HMM for a single phoneme. The first stage in the continuous-to-discrete mapping that FIGURE 5 Basic structure of a phonetic HMM.
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Page 174 is required for recognition is performed by the analysis box in Figure 1. Typically, the analysis consists of estimation of the short-term spectrum of the speech signal over a frame (window) of about 20 ms. The spectral computation is then updated about every 10 ms, which corresponds to a frame rate of 100 frames per second. This completes the initial discretization in time. However, the HMM, as depicted in this paper, also requires the definition of a discrete set of "output symbols." So, we need to discretize the spectrum into one of a finite set of spectra. Figure 5 depicts a set of spectral templates (known as a codebook) that represent the space of possible speech spectra. Given a computed spectrum for a frame of speech, one can find the template in the codebook that is "closest" to that spectrum, using a process known as vector quantization (Makhoul et al., 1985). The size of the codebook in Figure 5 is 256 templates. These templates, or their indices (from 0 to 255), serve as the output symbols of the HMM. We see in Figure 5 that associated with each state is a probability distribution on the set of 256 symbols. The definition of a phonetic HMM is now complete. We now describe how it functions. Let us first see how a phonetic HMM functions as a generative (synthesis) model. As we enter into state 1 in Figure 5, one of the 256 output symbols is generated based on the probability distribution corresponding to state 1. Then, based on the transition probabilities out of state 1, a transition is made either back to state 1 itself, to state 2, or to state 3, and another symbol is generated based on the probability distribution corresponding to the state into which the transition is made. In this way a sequence of symbols is generated until a transition out of state 3 is made. At that point, the sequence corresponds to a single phoneme. The same model can be used in recognition mode. In this mode each model can be used to compute the probability of having generated a sequence of spectra. Assuming we start with state 1 and given an input speech spectrum that has been quantized to one of the 256 templates, one can perform a table lookup to find the probability of that spectrum. If we now assume that a transition is made from state 1 to state 2, for example, the previous output probability is multiplied by the transition probability from state 1 to state 2 (0.5 in Figure 5). A new spectrum is now computed over the next frame of speech and quantized; the corresponding output probability is then determined from the output probability distribution corresponding to state 2. That probability is multiplied by the previous product, and the process is continued until the model is exited. The result of multiplying the sequence of output and transition probabilities gives the total probability that the input spectral sequence was "generated" by
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Page 175 that HMM using a specific sequence of states. For every sequence of states, a different probability value results. For recognition, the probability computation just described is performed for all possible phoneme models and all possible state sequences. The one sequence that results in the highest probability is declared to be the recognized sequence of phonemes. We note in Figure 5 that not all transitions are allowed (i.e., the transitions that do not appear have a probability of zero). This model is what is known as a "left-to-right" model, which represents the fact that, in speech, time flows in a forward direction only; that forward direction is represented in Figure 5 by a general left-to-right movement. Thus, there are no transitions allowed from right to left. Transitions from any state back to itself serve to model variability in time, which is very necessary for speech since different instantiations of phonemes and words are uttered with different time registrations. The transition from state 1 to state 3 means that the shortest phoneme that is modeled by the model in Figure 5 is one that is two frames long, or 20 ms. Such a phoneme would occupy state 1 for one frame and state 3 for one frame only. One explanation for the need for three states, in general, is that state 1 corresponds roughly to the left part of the phoneme, state 2 to the middle part, and state 3 to the right part. (More states can be used, but then more data would be needed to estimate their parameters robustly.) Usually, there is one HMM for each of the phonetic contexts of interest. Although the different contexts could have different structures, usually all such models have the same structure as the one shown in Figure 5; what makes them different are the transition and output probabilities. A HISTORICAL OVERVIEW HMM theory was developed in the late 1960s by Baum and colleagues (Baum and Eagon, 1967) at the Institute for Defense Analyses (IDA). Initial work using HMMs for speech recognition was performed in the 1970s at IDA, IBM (Jelinek et al., 1975), and Carnegie-Mellon University (Baker, 1975). In 1980 a number of researchers in speech recognition in the United States were invited to a workshop in which IDA researchers reviewed the properties of HMMs and their use for speech recognition. That workshop prompted a few organizations, such as AT&T and BBN, to start working with HMMs (Levinson et al., 1983; Schwartz et al., 1984). In 1984 a program in continuous speech recognition was initiated by the Advanced Research Projects Agency (ARPA), and soon thereafter HMMs were shown to be supe-
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Page 188 previously from one or more prototype speakers, methods have been developed for deriving a speech model for the new speaker through a simple transformation on the speech model of the prototype speakers (Furui, 1989; Kubala and Schwartz, 1990; Nakamura and Shikano, 1989). It is possible with these methods to achieve average SI performance for speakers who otherwise would have several times the error rate. One salient example of atypical speakers are nonnative speakers, given that the SI system was trained on only native speakers. In a pilot experiment where four nonnative speakers were tested in the RM domain in SI mode, there was an eight-fold increase in the word error rate over that of native speakers! The four speakers were native speakers of Arabic, Hebrew, Chinese, and British English. By collecting two minutes of speech from each of these speakers and using rapid speaker adaptation, the average word error rate for the four speakers decreased five-fold. Adding New Words Out-of-vocabulary words cause recognition errors and degrade performance. There have been very few attempts at automatically detecting the presence of new words, with limited success (Asadi et al., 1990). Most systems simply do not do anything special to deal with the presence of such words. After the user realizes that some of the errors are being caused by new words and determines what these words are, it is possible to add them to the system's vocabulary. In word-based recognition, where whole words are modeled without having an intermediate phonetic stage, adding new words to the vocabulary requires specific training of the system on the new words (Bahl et al., 1988). However, in phonetically based recognition, such as the phonetic HMM approach presented in this paper, adding new words to the vocabulary can be accomplished by including their phonetic pronunciations in the system's lexicon. If the new word is not in the lexicon, a phonetic pronunciation can be derived from a combination of a transcription and an actual pronunciation of the word by the speaker (Bahl et al., 1990a). The HMMs for the new words are then automatically compiled from the preexisting phonetic models, as shown in Figure 6. The new words must also be added to the grammar in an appropriate manner. Experiments have shown that, without additional training for the new words, the SI error rate for the new words is about twice that with training that includes the new words. Therefore, user-specified vocabulary and grammar can be easily incorporated into a speech
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Page 189 recognition system at a modest increase in the error rate for the new words. REAL-TIME SPEECH RECOGNITION Until recently, it was thought that to perform high-accuracy, real-time, continuous speech recognition for large vocabularies would require either special-purpose VLSI hardware or a multiprocessor. However, new developments in search algorithms have sped up the recognition computation at least two orders of magnitude, with little or no loss in recognition accuracy (Austin et al., 1991; Bahl et al., 1990b; Ney, 1992; Schwartz and Austin, 1991; Soong and Huang, 1991). In addition, computing advances have achieved two-orders-magnitude increase in workstation speeds in the past decade. These two advances have made software-based, real-time, continuous speech recognition a reality. The only requirement is that the workstation must have an A/D converter to digitize the speech. All the signal processing, feature extraction, and recognition search is then performed in software in real time on a single-processor workstation. For example, it is now possible to perform a 2000-word ATIS task in real time on workstations such as the Silicon Graphics Indigo R3000 or the Sun SparcStation 2. Most recently, a 20,000-word WSJ continuous dictation task was demonstrated in real time (Nguyen et al., 1993) on a Hewlett-Packard 735 workstation, which has about three times the power of an SGI R3000. Thus, the computation grows much slower than linear with the size of the vocabulary. The real-time feats just described have been achieved at a relatively small cost in word accuracy. Typically, the word error rates are less than twice those of the best research systems. The most advanced of these real-time demonstrations have not as yet made their way to the marketplace. However, it is possible today to purchase products that perform speaker-independent, continuous speech recognition for vocabularies of a few thousand words. Dictation of large vocabularies of about 30,000 words is available commercially, but the speaker must pause very briefly between words, and the system adapts to the voice of the user to improve performance. For more on some of the available products and their applications, the reader is referred to other papers in this volume. ALTERNATIVE MODELS HMMs have proven to be very good for modeling variability in time and feature space and have resulted in tremendous advances in
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Page 190 continuous speech recognition. However, some of the assumptions made by HMMs are known not to be strictly true for speechfor example, the conditional independence assumptions in which the probability of being in a state is dependent only on the previous state and the output probability at a state is dependent only on that state and not on previous states or previous outputs. There have been attempts at ameliorating the effects of these assumptions by developing alternative speech models. Below, we describe briefly some of these attempts, including the use of segmental models and neural networks. In all these attempts, however, significant computational limitations have hindered the full exploitation of these methods and have resulted only in relatively small improvements in performance so far. Segmental Models Phonetic segmental models form a model of a whole phonetic segment, rather than model the sequence of frames as with an HMM. Segmental models are not limited by the conditional assumption of HMMs because, in principle, they model dependencies among all the frames of a segment directly. Furthermore, segmental models can incorporate various segmental features in a straightforward manner, while it is awkward to include segmental features in an HMM. Segmental features include any measurements that are made on the whole segment or parts of a segment, such as the duration of a segment. There have been few segmental models proposed, among them stochastic segment models and segmental neural networks (to be described in the next section). Stochastic segment models view the sequence of feature vectors in a phonetic segment as a single long feature vector (Ostendorf et al., 1992). The major task is then to estimate the joint probability density of the elements in the feature vector. However, because the number of frames in a segment is variable, it is important first to normalize the segment to a fixed number of frames. Using some form of interpolation, typically quasi-linear, a fixed number of frames are generated that together form the single feature vector whose probability distribution is to be estimated. Typically, the distribution is assumed to be multidimensional Gaussian and its parameters are estimated from training data. However, because of the large size of the feature vector and the always limited amount of training data available, it is not possible to obtain good estimates of all parameters of the probability distribution. Therefore, assumptions are made to reduce the number of parameters to be estimated. Because segmental models model a segment directly, a segmentation of the speech into phonetic segments must be performed prior to
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Page 191 FIGURE 8 N-best paradigm for combining knowledge sources. modeling and recognition. In theory one would try all possible segmentations, compute the likelihood of each segmentation, and choose the one that results in the largest likelihood given the input speech. However, to try all possible segmentations is computationally prohibitive on regular workstations. One solution has been to use an HMM-based system to generate likely candidates of segmentations, which are then rescored with the segment models. Figure 8 shows the basic idea of what is known as the N-Best Paradigm (Ostendorf et al., 1991; Schwartz and Chow, 1990). First, an HMM-based recognition system is used to generate not only the top-scoring hypothesis but also the top N-scoring hypotheses. Associated with each hypothesis is a sequence of words and phonemes and the corresponding segmentation. For each of these N different segmentations and labelings, a likelihood is computed from the probability models of each of the segments. The individual segmental scores are then combined to form a score for the whole hypothesis. The hypotheses are then reordered by their scores, and the top-scoring hypothesis is chosen. Typically, the segmental score is combined with the HMM score to improve performance. Using the N-best paradigm with segmental models, with N = 100, has reduced word error rates by as much as 20 percent. The N-best paradigm has also been useful in reducing computation whenever one or more expensive knowledge sources needs to be combined, for example, cross-word models and n-gram probabilities for n > 2. N-best is a useful paradigm as long as the correct sentence has a high probability of being among the top N hypotheses. Thus far the paradigm has been shown to be useful for vocabularies up to 5,000 words, even for relatively long sentences. Neural Networks Whatever the biological motivations for the development of "artificial neural networks" or neural nets (Lippman, 1987), the utility of
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Page 192 FIGURE 9 Feedforward neural network. neural nets is best served by understanding their mathematical properties (Makhoul, 1991). We view a neural net as a network of interconnected simple nonlinear computing units and the output of a neural net as just a complex nonlinear function of its inputs. Figure 9 shows a typical feedforward neural network, that is, it has no feedback elements. Although many different types of neural nets have been proposed, the type of network shown in Figure 9 is used by the vast majority of workers in this area. Shown in the figure is a three-layer network, with each layer consisting of inputs, a number of nodes, and interconnecting weights. (The term "hidden" has been used to describe layers that are not connected directly to the output.) All the nodes are usually identical in structure as shown in the figure. The inputs u to a node are multiplied by a set of weights v and summed to form a value z. A nonlinear function of z, g(z), is then computed. Figure 9 shows one of the most typical nonlinear functions used, that of a sigmoid. The output y of the network is then a nonlinear function of the input vector. In general, the network may have a vector of outputs as well. There are two important mathematical properties of neural nets that form the cornerstones upon which successful applications have been developed. The first is a function approximation property: it
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Page 193 has been shown that a two-layer neural net is capable of approximating any function arbitrarily closely in any finite portion of the input space (Cybenko, 1989). One could use more than two layers for the sake of parsimony, but this property says that two layers are sufficient (with a possibly large number of nodes in the hidden layer) to approximate any function of the input. For applications where some nonlinear function of the input is desired, the neural net can be trained to minimize, for example, the mean squared error between the actual output and the desired output. Iterative nonlinear optimization procedures, including gradient descent methods, can be used to estimate the parameters of the neural network (Rumelhart et al., 1986). The second important property of neural nets relates to their use in classification applications: a neural net can be trained to give an estimate of the posterior probability of a class, given the input. One popular method for training the neural net in this case is to perform a least squares minimization where the desired output is set to 1 when the desired class is present at the input and the desired output is set to 0 otherwise. One can show that by performing this minimization the output will be a least squares estimate of the probability of the class given the input (White, 1989). If the classification problem deals with several classes, a network is constructed with as many outputs as there are classes, and, for a given input, the class corresponding to the highest output is chosen. As mentioned above, estimating the parameters of a neural net requires a nonlinear optimization procedure, which is very computationally intensive, especially for large problems such as continuous speech recognition. Neural nets have been utilized for large-vocabulary, continuous speech recognition in two ways: • They have been used to model the output probability density for each state in an HMM (Renals et al., 1992). • Segmental neural nets have been used to model phonetic segments directly by computing the posterior probability of the phoneme given the input (Austin et al., 1992). In both methods the neural net system is combined with the HMM system to improve performance. In the case of segmental neural nets, the N-best paradigm is used to generate likely segmentations for the network to score. Using either method, reductions in word error rate by 10 to 20 percent have been reported. Other neural net methods have also been used in various continuous speech recognition experiments with similar results (Hild and Waibel, 1993; Nagai et al., 1993).
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Page 194 CONCLUDING REMARKS We are on the verge of an explosion in the integration of speech recognition in a large number of applications. The ability to perform software-based, real-time recognition on a workstation will no doubt change the way people think about speech recognition. Anyone with a workstation can now have this capability on their desk. In a few years, speech recognition will be ubiquitous and will enter many aspects of our lives. This paper reviewed the technologies that made these advances possible. Despite all these advances, much remains to be done. Speech recognition performance for very large vocabularies and larger perplexities is not yet adequate for useful applications, even under benign acoustic conditions. Any degradation in the environment or changes between training and test conditions causes a degradation in performance. Therefore, work must continue to improve robustness to varying conditions: new speakers, new dialects, different channels (microphones, telephone), noisy environments, and new domains and vocabularies. What will be especially needed are improved mathematical models of speech and language and methods for fast adaptation to new conditions. Many of these research areas will require more powerful computing resourcesmore workstation speed and more memory. We can now see beneficial utility for a two-orders-magnitude increase in speed and in memory. Fortunately, workstation speed and memory will continue to grow in the years to come. The resulting more powerful computing environment will facilitate the exploration of more ambitious modeling techniques and will, no doubt, result in additional significant advances in the state of the art. REFERENCES Asadi, A., R. Schwartz, and J. Makhoul, ''Automatic Detection of New Words in a Large Vocabulary Continuous Speech Recognition System," IEEE International Conference on Acoustics, Speech, and Signal Processing, Albuquerque, pp. 125128, April 1990. Austin, S., R. Schwartz, and P. Placeway, "The Forward-Backward Search Algorithm," IEEE International Conference on Acoustics, Speech, and Signal Processing, Toronto, Canada, pp. 697-700, 1991. Austin, S., G. Zavaliagkos, J. Makhoul, and R. Schwartz, "Speech Recognition Using Segmental Neural Nets," IEEE International Conference on Acoustics, Speech, and Signal Processing, San Francisco, pp. 1-625-628, March 1992. Bahl, L. R., F. Jelinek, and R. L. Mercer, "A Maximum Likelihood Approach to Continuous Speech Recognition," IEEE Trans. Pat. Anal. Mach. Intell., Vol. PAMI-5, No. 2, pp. 179-190, March 1983.
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Representative terms from entire chapter: