Efficient Computer Interfaces Using Continuous Gestures, Language Models, and Speech Keith Vertanen July 30 th, 2004

The problem Speech recognizers make mistakes Correcting mistakes is inefficient  140 WPM Uncorrected dictation  14 WPMCorrected dictation, mouse/keyboard  32 WPMCorrected typing, mouse/keyboard Voice-only correction is even slower and more frustrating

Research overview Make correction of dictation:  More efficient  More fun  More accessible Approach:  Build a word lattice from a recognizer’s n-best list  Expand lattice to cover likely recognition errors  Make a language model from expanded lattice  Use model in a continuous gesture interface to perform confirmation and correction

Building lattice Example n-best list: 1: jack studied very hard 2: jack studied hard 3: jill studied hard 4: jill studied very hard 5: jill studied little

Insertion errors

Acoustic confusions Given a word, find words that sound similar Look pronunciation up in dictionary: studieds t ah d iy d Use observed phone confusions to generate alternative pronunciations:s t ah d iy d s ao d iy s t ah d iy … Map pronunciation back to words: s t ah d iy d studied s ao d iysaudi s t ah d iystudy

Acoustic confusions: “Jack studied hard”

Language model confusions: “Jack studied hard” Look at words before or after a node, add likely alternate words based on n-gram LM

Expansion results (on WSJ1)

Probability model Our confirmation and correction interface requires probability of a letter given prior letters:

Probability model Keep track of possible paths in lattice Prediction based on next letter on paths Interpolate with default language model Example, user has entered “the_cat”:

Handling word errors Use default language model during entry of erroneous word Rebuild paths allowing for an additional deletion or substitution error Example, user has entered “the_cattle_”:

Evaluating expansion Assume a good model requires as little information from the user as possible

Results on test set Model evaluated on held out test set (Hub1) Default language model  2.4 bits/letter  User decides between 5.3 letters Best speech-based model  0.61 bits/letter  User decides between 1.5 letters

“To the mouse snow means freedom from want and fear”

Questions?