An 86,000-Word Recognizer Based on Phonemic Models

An 86,000-Word Recognizer Based on Phonemic Models M. Lennig, V. Gupta, P. Kenny, P. Mermelstein, D. O'Shaughnessy IN RS-T414communications 3 Place du Commerce Montreal, Canada H3E 1H6 (514) 765-7772 Abstract We have developed an algorithm for the automatic conversion of dictated English sentences to written text, with essentially no restriction on the nature of the mate- rial dictated. We require that speakers undergo a short training session so that the system can adapt to their individual speaking characteristics and that they leave brief pauses between words. We have tested our algo- rithm extensively on an 86,000 word vocabulary (the largest of any such system in the world) using nine speakers and obtained word recognition rates on the or- der of 93Uo. Introduction Most speech recognition systems, research and com- mercial, impose severe restrictions on the vocabulary that may be used. For a system that aims to do speech- to-text conversion, this is a serious limitation since the speaker may be unable to express himself in his own words without leaving the vocabulary. From the outset we have worked with a very large vocabulary, based on the 60,000 words in Merriam Webster's Seventh New Collegiate Dictionary. We have augmented this num- ber by 26,000 so that at present the probability of en- countering a word not in the vocabulary in a text cho- sen at random from a newspaper, magazine or novel is less than 2% [25]. (More than 80% of out-of-vocabulary words are proper names.) Our vocabulary is thus larger than that of any other English language speech-to-text system. IBM has a real-time isolated word recognizer with a vocabulary of 20,000 words [1] giving over 95% word recognition on an office correspondence task. The perplexity [16] of this task is about 200; the corresponding figure in our case is 700. There is only one speech recognition project in the world having a larger vocabulary than ours; it is being developed by IBM France [20] and it requires that the user speak in isolated syllable mode, a constraint which may be reasonable in French but which would be very unnatural in English. Briefly, our approach to the problem of speech recog- nition is to apply the principle of naaximum a poste- riori probability (MAP) using a stochastic model for the speech data associated with an arbitrary string of words. The model has three components: (i) a language model which assigns prior probabilities to word strings, (ii) a phonological component which assigns phonetic transcriptions to words in the dictionary and (iii) an acoustic-phonetic model which calculates the likelihood of speech data for an arbitrary phonetic transcription. Language Modeling We have trained a trigram language model, which assigns a prior probability distribution to words in the vocabulary based on the previous two words uttered, on 60 million words of text consisting of 1 million words from the Brown Corpus [11], 14 million from Hansard (the record of House of Commons debates), 21 million from the Globe and Mail and 24 million from the …

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