Abstract
An attribute-based speech recognition system is described. A speech pre-processor receives input speech and produces a sequence of acoustic observations representative of the input speech. A database of context-dependent acoustic models characterize a probability of a given sequence of sounds producing the sequence of acoustic observations. Each acoustic model includes phonetic attributes and suprasegmental non-phonetic attributes. A finite state language model characterizes a probability of a given sequence of words being spoken. A one-pass decoder compares the sequence of acoustic observations to the acoustic models and the language model, and outputs at least one word sequence representative of the input speech.
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