ows a block diagram of a speech-recognition system combined with the invention disclosed herein in a speech synthesis system to form an automated natural-language-based control system.
FIG. 3 shows a graphical representation of the predetermined relationships between recognizable words which form the language and which the system is capable of recognizing.
FIG. 4 shows an array of data elements that comprise the dictionary of keywords of the pre-specified, well-defined language. Each of the data elements points to an element in a linked list.
FIG. 5 shows how the array of data elements shown in FIG. 4 can be stored in the memory of a computer.
FIGS. 6, 7, and 8 show flowcharts depicting the steps of the method of the prefeffed embodiment.
FIG. 9A, 9B and 9C show a source code implementation of data structures used to practice the invention.
The present invention contemplates that input from a user has already been recognized by an input device, such as a speech recognition system. The prior art speech recognition system depicted in FIG. 1 is briefly described herein for purposes of completeness and to facilitate an understanding for the invention described hereinafter.
Referring to FIG. 1, input speech (1-2) is subject to spectral analysis (1-4) the result of which is a numerical representation of frequency components, duration, etc. of speech components all of which is well known in the art. Spectral analysis is a numerical method of analyzing speech, however, other methods that might be accomplished by a digital signal processor, single chip computer, or other appropriate computational device could be used as well. The result of the spectral analysis (1-4) processing is a numerical representation (1-6) of the speech that is pattern matched (1-8) to determine if the processed speech (1-6) is recognizable.
Pattern matching (1-8) essentially compares numerical representations of recognized speech (1-6) to stored numerical representations, or models, (1-8) of words that are to be recognized. If the stored models (1-8) do not substantially match the spectral model (1-6), the analyzed speech is not recognized. If a reference model (1-8) substantially matches the spectrally analyzed input speech (1-6), the words are determined or considered to be matched. The closeness of the representation match will, of course, be a function of how fault tolerant the speech recognition needs to be and is a design choice.
Once a match between a reference model (1-8) and an input word (1-6) is determined to have been made, an output signal is produced indicating that the particular word that has been matched. The matched word is delivered to a post processor (1-10).
The invention described herein is a method for post-processing recognized speech.
The invention requires that every word to be recognized by the system, first be specified in advance. These specified words form a set of such words, which together can be used to express some input that must be acted upon. If a recognizable word is not a keyword within the list of such words, the system cannot and will not use such words in its processing.
By way of example, if the system shown in FIG. 2 were to be used by an airline for automatically accepting travel reservations without human intervention, the set of language keywords might be specified to include words such as: “domestic” “international” “first” “coach” “class” as well as the names of destination cities served by the airline, dates, days of the week, months of the year, and times of day. In actual use, a phone call to the airline by a customer received and processed by the invention 2-4 would cause the system to query the customer to specify what his or her travel plans were. An airline customer wishing to make reservations to fly to a particular destination city, on a particular day or date, would likely utter one or more of the aforementioned keywords. Detection and identification of a sufficient number of keywords eventually permits the system to render a decision as to the customer's intended statement. For example, consider 