RECOGNITION OF PROSODIC FACTORS AND DETECTION OF LANDMARKS FOR IMPROVEMENTS TO CONTINUOUS SPEECH RECOGNITION SYSTEMS
This thesis examines the usefulness of including prosodic and phonetic context information in the phoneme model of a speech recognizer. This is done creating a series of prosodic and phonetic models and then comparing the log likelihoods of each model. The comparison of log likelihoods shows that both prosodic and phonetic context information improve the phoneme model for most phonemes. The prosodic and phonetic context information is then modeled in two separate recognizers to show that while phonetic context modeling does provide some useful information for the recognizer, prosody provides much more. This thesis also experiments with the detection of landmarks, the transitions between two phonemes, using binary classification. The experiments presented show that landmarks can be detected with high accuracy and are useful for phone classification. Introduction Humans can interpret and understand spoken language with relative ease. Computers, however , struggle with the task of recognizing continuous speech. Part of the problem is that speech recognizers implemented on computers are not robust to environmental and acoustic variations. For example, a speech recognizer trained under quiet conditions fails miserably when any amount of background noise is added. A speech recognizer trained on a specific person's voice will not perform as well for a different talker. Even the choice of microphone affects the performance of a recognizer. The accuracy of a recognizer trained on speech recorded with one microphone will decrease if the original microphone is replaced with a different one. Another problem is that speech itself is variable. No word or sound is produced in exactly the same way twice. Another factor that plays into the poor performance of speech recognizers is that natural human conversational speech contains thousands of words; many of which have similar pronunciations. When a speech recognizer is trained and tested on a database containing only a few isolated, discrete words, recognition rates are high. Liu et al. [1], Samouelian [2], and Li-Peng et al. [3] each use different methods to implement isolated word recognition systems and each report that their different recognizers can identify over 90% of the words correctly. However, the word error rate (WER) increases significantly when attempts are made to recognize continuous conversational speech. Liu et al. [4] and Eide et al. [5] each report WER's of 50% or more when performing recognition experiments on corpora of 1 conversational telephone speech. The research presented in this thesis attempts to address many of …