Abstract:
A system that identifies recognized words from a voice recognition system that have the lowest possibility of being correct, and flagging those words on a user interface, to help with proofreading.

Description:
BACKGROUND  
         [0001]    Many different dictation engines are known, including, but not limited to, those made by Dragon Systems, IBM, and others. These dictation engines typically include a vocabulary, and attempt to match the voice being spoken to the vocabulary.  
           [0002]    It may be difficult to proofread the dictated text. Speech recognition technology relies heavily on the acoustic characteristics of words, i.e. the sound of the words that are uttered. Therefore, it is not uncommon for the recognition engine to recognize words that sound similar to the correct word but are nonsensical in context. This may make proofreading tedious, especially since other clues such as incorrect spellings, do not exist.  
           [0003]    The dictation engines commonly use word sequences to select the best word that matches the spoken word, based on models of the language. However, the best choice might still be incorrect. Final proofreading is used for the last proofreading operation.  
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0004]    These and other aspects will now be described in detail with reference to the accompanying drawings, wherein:  
         [0005]    [0005]FIG. 1 shows a block diagram of a computer running a speech recognition engine;  
         [0006]    [0006]FIG. 2 shows a flowchart of operation to identify and produce an indication showing likely misrecognition candidates; and  
         [0007]    [0007]FIG. 3 shows an exemplary user interface with the likely misrecognition candidates being indicated. 
     
    
     DETAILED DESCRIPTION  
       [0008]    The present system teaches a technique of using confidence levels generated by the speech recognition engine to analyze a document. The user interface is also modified to provide a view of the document which includes information about the confidence level. In an embodiment, this system may use lists of words which are already produced by the dictation engine.  
         [0009]    [0009]FIG. 1 shows a basic embodiment of the system. A computer system  100  includes an audio processing unit  102  which has a connection to a microphone  104 . The audio processing unit  102  may include, for example, a sound card. The audio processing unit  102  is connected via a bus, e.g. via the PCI bus, to processor  110  which is driven by stored instructions in memory  112 . The processor may also include associated working memory  114 , which may include random access memory or RAM of various types, including internal RAM to the processor. The processor operates based on instructions in a known way.  
         [0010]    In an embodiment, the stored instructions may include a commercial dictation engine, such as the ones available from Lernout and Hauspie, Dragon Systems, IBM and/or Phillips.  
         [0011]    When recognizing an utterance, speech engines often produce two different items. First, an Alts List may be produced. The Alts list includes at least one, but usually more than one, recognition candidate for each recognized word or phrase. Commonly, the recognition candidate that has the highest score is taken as the best candidate, and eventually inserted into the text. Various techniques, including word sequence modelling from a statistical language model may be used along with other models, such as an acoustic model to produce confidence scores.  
         [0012]    Each recognition candidate, whether a phrase or a single word, is associated with a corresponding confidence value. The confidence value quantifies the confidence of the recognizer that the word or phrase correctly corresponds with the user utterance. Confidence values are often based on a combination of the language model that is used, and the acoustic model that does the scoring. The best solution may be obtained from both language model and each acoustic model scores. However, different techniques may be used to find the best match.  
         [0013]    While the different dictation engines may have different names for these variables, virtually all dictation engines are believed to produce a list of the different candidates and somehow score the likelihood that the current word is the correct candidate.  
         [0014]    The present system uses these variables to identify situations where it is likely that recognitions error have occurred. The system operates in conjunction with the dictation recognition engine which is shown in  200 . At  205 , the system first recognizes a situation where the best recognition has a confidence level less than a predefined threshold. For example, the predefined threshold may define the confidence level, e.g., less than 50 percent correct, or less than 70 percent correct. These values are used to form a first list, called list A. Another technique may use a percentile approach, where the lowest 5 percentile of confidence levels are identified.  
         [0015]    At  210 , the system identifies two alternatives which have very close scores, e.g., close enough that accurate detection of one or the other might not be possible. Again, this may use a system of percentile ratings. The scores lying in the top 5 percentile closest scores are taken as unusually close confidence ratings. These values obtained at  210  are used to form a second list, referred to as list B.  
         [0016]    Hence, during the dictation, list A. may include a list of all words or phrases with the lowest confidence levels. This aim may be arranged in an ascending sort, such as in the following:  
         [0017]    Pea  30   
         [0018]    Farm  31   
         [0019]    Car  32   
         [0020]    Truck  35 .  
         [0021]    List B is also formed during the dictation. List B corresponds to a descending sort of all words or utterances whose top two or three recognition candidates vary within a margin that is very narrow as described above. The entries in list B might look like the following.  
         [0022]    Eight  85   
         [0023]    Ate  83   
         [0024]    Bait  80 .  
         [0025]    By following the operations in  205  and  210 , lists a and B. are formed for the entire document.  
         [0026]    At  215 , the list A. and list B. words are identified. The user interface is modified to show at least some of the list A. and list B. words in the document. For example, a user can select to have more words shown, e.g., all the words in both of lists A and B. As an alternative, only some of these words may be shown in the document. Since the lists are ordered, only the top x% of the words may be selected, in another embodiment.  
         [0027]    In one embodiment, shown in FIG. 3, the words on the list may be highlighted within the document. The highlighting may be carried out by underlining with a squiggly line, which denotes that these words are the most likely words to be incorrect. Other highlighting techniques may use different colors for the words, different fonts for the words, or anything else that might indicate that the words are likely misrecognition candidates. By doing this, the users may be advised of likely misrecognitions, thereby making it easier to proofread such a document.  
         [0028]    Although only a few embodiments have been disclosed in detail above, other modifications are possible. For example, the alteration of the user interface may be carried out to show different things other then squiggly lines. The words may be highlighted or shown in some other form. In addition, other techniques may be used besides these described above to obtain either alternative lists, or additional lists. All such modifications are intended to be encompassed within the following claims, in which: