Isolated Word Speech Recognition System Research Articles

Signal-dependent matching for isolated word speech recognition systems

A new approach to the design of IWSR systems is proposed in this paper. This involves a dynamic matching strategy based on the nature of the input speech segment. This is called signal-dependent matching. The computational complexity in the implementation of the proposed algorithm is significantly reduced by adopting a two stage approach in matching. In the first stage, the warping path between the test utterance and a reference utterance is determined. In the second stage, the distance between the utterances is computed along the path. There will be a slight degradation in the performance of a two stage approach as compared to the single stage approach, but this can be tolerated in view of the significant computational advantage. The performance degradation is more than compensated by the signal-dependent matching strategy in the second stage. To measure the improvement in the recognition performance, a new index of performance is defined, that reflects the characteristics of the distance matrix for a given vocabulary, rather than the characteristics of the confusion matrix. The performance of the signal-dependent matching algorithm is significantly better than the standard dynamic time warping matching algorithm for confusable as well as nonconfusable vocabulary. We also develop a signal-dependent matching algorithm, which takes into account some distortions in the input speech. As an example we offer the agorithm twice the same test utterance, once undistorted, once after a distortion. Our research until now indicates a improvement in automatic isolated word speech recognition systems while using signal-dependent parameter measuring and signal dependent matching.

On-line recognition of spoken words from a large vocabulary

It is demonstrated in this paper that a real-time, large-vocabulary, isolated-word speech recognition system can effectively be implemented using the following two-stage organization: 1. (1) conversion of the speech signal into phonemic transcriptions, 2. (2) recognition of phonemic transcriptions by advanced searching methods. A comparison of several alternatives for the first stage has indicated that the best accuracy is achieved by the learning-subspace method For the second stage the authors recommend fast string searching by redundant hash addressing combined with subsequent probabilistic analysis. The above system has been implemented in a minicomputer environment.

A speaker-independent discrete utterance recognition system, combining deterministic and probabilistic strategies

A speaker-independent isolated word speech recognition system is discussed in which an unknown utterance is described by a set of speech feature measurements and then compared with a reference set of the same measurements obtained during a training procedure with a population of speakers. To reduce significantly the number of word confusions, a segmentation procedure is used. As a result, the whole vocabulary is divided into a number of subgroups characterized by a certain phonetic structure which is represented by the sequence of four types of segments. Each of these segments reflects quite reliably certain speaker independent events in the speech signal. Thus, the subgroup may consist of the reference sets for several confusable words, on the other hand, each word is represented by its reference sets in a number of the subgroups. The system was evaluated Using a 20-word vocabulary (including ten digits). A mean recognition accuracy of about 95 percent was obtained. The tradeoff between quite precise segmentation, which leads to a very bulky system if it is speaker-independent, and rough segmentation, which in some cases does not reduce the number of confusable words too much, is also discussed.

A Probabilistic Model for the Performance of Word Recognizers

This paper develops a probabilistic model to account for the error-rate behavior of isolated-word speech-recognition systems. It examines two kinds of errors, confusion error, an a priori characterization of a recognizer, which measures differences between words, and recognition (rank) error, an a posteriori characterization, which, in addition to taking into account differences between words, accounts for differences between different tokens of the same word. It is shown that these kinds of errors can be modeled by describing recognition trials as Bernoulli trials. Good models of error-rate behavior as a function of vocabulary size can be obtained if the distributions of confusion and recognition (rank) number are considered to be mixtures of binomial distributions. The data obtained from a recent experiment in isolated-word recognition with a large vocabulary (1109 words) are used to evaluate the model. Experimental error-rate functions obtained from each of six talkers and three partitions of the vocabulary are fit by means of an optimization algorithm to model functions based on mixture distributions. The results indicate that two-way mixture distributions account quite well for the experimental performance results.

Comparative study of nonlinear time warping techniques in isolated word speech recognition systems

In this paper, the effects of two major design choices on the performance of an isolated word speech recognition system are examined in detail. They are: 1) the choice of a warping algorithm among the Itakura asymmetric, the Sakoe and Chiba symmetric, and the Sakoe and Chiba asymmetric, and 2) the size of the warping window to reduce computation time. Two vocabularies were used: the digits (zero, one,..., nine) and a highly confusable subset of the alphabet (b, c, d, e, g, p, t, v, z). The Itakura asymmetric warping algorithm appears to be slightly better than the other two for the confusable vocabulary. We discuss the reasons why the performance of the algorithms is vocabulary dependent. Finally, for the data used in our experiments, a warping window of about 100 ms appears to be optimal.

An efficient method for testing large vocabulary automatic speech recognition systems

The performance of automatic speech recognition systems is commonly measured by using large quantities of natural speech as a benchmark. For recognizers which accept large vocabularies, or when many alternative vocabularies are of interest, this method requires an unreasonably large natural speech corpus. Synthetic speech which incorporates variations in pronunciation is an alternative in these cases. This approach was used to evaluate the performance of a speaker trained, isolated word speech recognition system [H. Murveit, M. Lowy, and R. W. Brodersen, J. Acoust. Soc. Am. Suppl. 1 69, S8 (1981)]. Each word to be recognized was modeled as a sequence of segments with each segment being variable length and having a uniform spectrum with added noise. A few tokens of natural speech were used to evaluate the parameters of the model. The model was then used to produce synthetic tokens for testing the recognizer. Good correlation was obtained between the confusion matrices for synthetic and natural speech when recognized. [Work supported in part by DARPA.]

An architecture of an MOS‐LSI speech recognition system using dynamic programming

In the past several years, a number of very accurate, isolated word speech recognition systems based on dynamic programming techniques have been designed and tested. However, as these techniques are computationally intensive, commercial systems using dynamic time warping have been costly. We have designed an architecture which exploits the capabilities of custom MOS‐LSI designs to implement a complete speech recognition system. This system would operate in real time using dynamic time warping, yet it would only require 4–5 integrated circuits for a moderate (50–200 word) vocabulary. This system is designed to be expandable so that larger vocabularies can be used by including additional IC's in parallel with the others. The integrated circuits which are required are two custom‐designed chips, a memory IC, and a low‐performance microcomputer for overall control. The custom chips include a front end processor for spectral analysis (currently a switched‐capacitor filter bank) with an endpoint detector, and an IC to implement the dynamic programming algorithm. The architecture of the custom chips has been defined so that adequate performance can be obtained from a standard MOS process. A connected speech recognition algorithm that is based on the use of the above IC's has been developed and will be described. It requires additional processing in the low‐speed microcomputer to perform a second level of dynamic programming.

A new training method for isolated word recognition

One of the most important operations in isolated word speech recognition systems is the method used to obtain the word reference templates. For speaker‐trained systems, the techniques that have been used include casual training, averaging, and use of statistical pattern recognition clustering methods. In a recent study Rabiner and Wilpon showed that the statistical techniques, when combined with the technique of averaging the autocorrelation coefficients of all tokens within the cluster, provided a reliable, robust set of reference templates. The only drawback to this method was the extensive, burdensome training required for the statistical analysis. Since the statistical training method could not be used in most practical situations, techniques were investigated for obtaining a simplified, robust training procedure which would incorporate many of the ideas of the statistical approach. Such a training method is described in this talk. The advantages of this new training procedure, over (previously used) casual training include: ease of use, robustness of the templates, minimized computation in the recognition algorithm, normalized durations of all references, and recognition accuracy comparable to previous systems with double the number of templates.

A simplified, robust training procedure for speaker trained, isolated word recognition systems

One of the most important operations in isolated word speech recognition systems is the method used to obtain the word reference templates. For speaker trained systems, the techniques that have been used include casual training, averaging, and use of statistical pattern recognition clustering methods. In a recent study, Rabiner and Wilpon showed that the statistical techniques, when combined with the technique of averaging the autocorrelation coefficients of all tokens within the cluster, provided a reliable, robust set of reference templates. The only drawback to this method was the extensive, burdensome training required for the statistical analysis. Since the statistical training method could not be used in most practical situations, techniques were investigated for obtaining a simplified, robust training procedure which would incorporate many of the ideas of the statistical approach. Such a training method is described in this paper. The advantages of this new training procedure, over (previously used) casual training include: ease of use, robustness of the templates, minimized computation in the recognition algorithm, normalized durations of all references, and recognition accuracy comparable to previous systems with double the number of templates.