Speech recognition basically means talking to a computer, having it recognize what we are saying. This process fundamentally functions as a pipeline that converts PCM (Pulse Code Modulation) digital audio from a sound card into recognized speech. Speech recognition technology has evolved for more than 40 years, spurred on by advances in signal processing, algorithms, architectures, and hardware. During that time it has gone from a laboratory curiosity to an art, and eventually to a full-fledged technology that is practiced and understood by a wide range of engineers, scientists, linguists, psychologists, and systems designers. Over those 4 decades, the technology of speech recognition has evolved, leading to a steady stream of increasingly more difficult asks which have been tackled and solved
The sentence-level match module uses a language model (i.e., a model of syntax and semantics) to determine the most likely sequence of words. Syntactic and semantic rules can be specified, either manually, based on task constraints, or with statistical models such as word and class N-gram probabilities. Search and recognition decisions are made by 502 considering all likely word sequences and choosing the one with the best matching score as the recognized sentence.
Almost every aspect of the continuous speech recognizer of Figure 1 has been studied and optimized over the years. As a result, we have obtained a great deal of knowledge about how to design the feature analysis module, how to choose appropriate recognition units, how to populate the word lexicon, how to build acoustic word models, how to model language syntax and semantics, how to decode word matches against word models, how to efficiently determine a sentence match, and finally how to eventually choose the best recognized sentence
