Vishwa Gupta

A language model for very large-vocabulary speech recognition

Abstract We apply a trigram language model to an 86 000-word vocabulary speech recognition task. The recognition task consists of paragraphs chosen arbitrarily from a variety of sources, including newspapers, books, magazines, etc. The trigram language model parameters correspond to probabilities of words conditioned on the previous two words. The number of parameters to be estimated is enormous: 86 000 3 parameters in our case. Even a training set consisting of 60 million words is too small to estimate these parameters reliably. Parameter estimates using relative frequencies would assign a value of zero to a large fraction of the parameters. Many algorithms have been proposed to estimate probabilties of events not observed in the training text. We propose here a simple algorithm for estimating the probabilities of such events using Turings formula. The resulting trigram language model reduces the acoustic recognition errors by 60%. We also show that the effectiveness of the trigram language model for correcting an acoustic word recognition error depends on whether or not the neighbouring word contexts occur in the training text corpus for the language model.

Decision rules for speaker-independent isolated word recognition

This study compares the recognition rates attainable with the aid of two different methods of generating reference templates from training words and two different decision rules. The test enviroment consists of isolated words from a small vocabulary spoken by a large number of speakers over the public telephone system. Experiments performed show that the use of individual word templates for references together with the k-nearest neighbor decision procedure substantially improves the performance in isolated word recognition. We attempted to minimize the computations involved in the k-nearest neighbor decision procedure by assuming that the dynamic time-warp distance was a metric, which would allow use of a 1-nearest neighbor decision rule with appropriately relabeled reference data. Results indicate that this step leads to an error rate exceeding that obtainable with the 1-nearest neighbor rule on the original nonrelabeled data.

Large Vocabulary Isolated Word Recognition

Many applications require recognition of spoken isolated words or phrases from a large vocabulary. For example, the goal of the 86000-word recognizer at INRS-Telecommunications [14] is to transcribe speech spoken as a sequence of isolated words. The sentences to be read are chosen arbitrarily from a variety of sources, including newspapers, books, magazines, etc. Another example is the StockTalk system running at BNR Montreal [24] which dispenses real time stock quotes by voice over the telephone for stocks traded in New York, Toronto and NASDAQ stock exchanges. The vocabulary for this system consists of words or phrases spoken in isolation. This system requires speaker-independent recognition over the telephone, while the first example requires speaker-dependent recognition over high quality microphones.