Abstract:
A computer-implemented dynamic pronunciation system and method that includes a dictionary storage unit for containing word pronunciation rules. A dictionary generation unit determines a first set of possible pronunciation rules for a pre-selected word. A neural network accepts word spelling as an input and generates at least one pronunciation rule as an output. The pronunciation rule from the neural network is used within the first set of possible pronunciation rules for the pre-selected word to form a pronunciation dictionary.

Description:
RELATED APPLICATION  
       [0001]    This application claims priority to U.S. provisional application Serial No. 60/258,911 entitled “Voice Portal Management System and Method” filed Dec. 29, 2000. By this reference, the full disclosure, including the drawings, of U.S. provisional application Serial No. 60/258,911 are incorporated herein. 
     
    
     
       FIELD OF THE INVENTION  
         [0002]    The present invention relates generally to computer speech processing systems and more particularly, to computer systems that recognize speech.  
         BACKGROUND AND SUMMARY OF THE INVENTION  
         [0003]    Pronunciation dictionaries have been used to assist in the recognition of speech. These pronunciation dictionaries associate how a word is to be pronounced with the spelling of the word. Traditional techniques for generating accurate pronunciation for a dictionary are accomplished by actual recordings of user speech. The traditional techniques also build acoustic models (such as Hidden Markov Models) to generate the pronunciations. However, composing necessary acoustic models for different vocabulary set is both a cumbersome and time-consuming process. Moreover, when a large amount of data are used, the pronunciation rules generated by these acoustic models may contradict each other, because these rules are statically input into the system.  
           [0004]    Further areas of applicability of the present invention will become apparent from the detailed description provided hereinafter. It should be understood however that the detailed description and specific examples, while indicating preferred embodiments of the invention, are intended for purposes of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0005]    The present invention will become more fully understood from the detailed description and the accompanying drawings, wherein:  
         [0006]    [0006]FIG. 1 is a block diagram depicting a neural network of the present invention that is used in synthesizing speech;  
         [0007]    [0007]FIG. 2 is a block diagram depicting the use of a neural network within a speech recognition system;  
         [0008]    [0008]FIG. 3 is an exemplary structure of a neural network of the present invention used in recognizing speech; and  
         [0009]    [0009]FIG. 4 is a flow chart depicting an exemplary operational scenario of the present invention. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT  
       [0010]    [0010]FIG. 1 depicts a dynamic pronunciation dictionary system  30  of the present invention. The system  30  utilizes a neural network  34  to generate letter to sound rules for use in a speech recognition system. The neural network is provided raw data (e.g., new words) for training. The spelling of the words are provided as input  26  to the neural network  34 , and the neural network  34  is trained in combination with the defined phonemes of a vocabulary set to generate new rules and to tune existing rules which together indicate how the input words are to be pronounced. It should be understood that the neural network  34  may generate any basic pronunciation unit (such as a phoneme) within the system  30  of the present invention.  
         [0011]    The generated letter to sound rules indicate that for a given spelling of an input word, the following phonemes may be used to pronounce the input word. The generated letter to sound rules are included into a corpus  28 , such as a pronunciation dictionary and used in an operational application to recognize user input speech. Language models (such as Hidden Markov models) are constructed from the rules of the corpus  28 .  
         [0012]    More specifically, the present invention trains the neural network  34  to generate accent-specific pronunciation rules. For example, the neural network may generate United States mid-western English speaking accent pronunciation rules, United States southern English speaking accent pronunciation rules, etc. The present invention may utilize these different pronunciation rules in the speech recognition system  43  to determine the accent of a user. The user&#39;s accent may be initially recognized by examining at least several words of the user speech to determine which accent pronunciation rules best recognizes the user speech. After the accent has been determined, the correct accent pronunciation rules (such as the United States mid-western English speaking accent pronunciation rules) may be used to better recognize the speech input of the user.  
         [0013]    Thus, the neural network  34  of the present invention tunes rules from a pronunciation dictionary according to accents provided. When a user&#39;s accent is determined, the neural network  34  can tune the pronunciation dictionary that is used in the operational application by adjusting the rules and creating new rules according to the accent. The original rules of the pronunciation dictionary may also be used as input to operational application.  
         [0014]    [0014]FIG. 2 depicts the system  30  in a more detailed embodiment of the present invention. With reference to FIG. 2, the system  30  contains an initial dictionary  32  that acts as a “starting point” for pronunciation with letter to sound rules for word pronunciation and tokenization rules for partitioning words into basic sounds. The initial dictionary  32  is prepared to be tuned by the pronunciation with letter to sounds rules for word pronunciation and tokenization rules for partitioning words into basic sounds. The initial dictionary also contains basic, predefined pronunciations, in terms of phonemes, which are previously created by acoustic models or pronunciation dictionaries. The neural network  34  allows machine learning that adapts to variations among users&#39; pronunciations and can accommodate different user accents.  
         [0015]    Input specific to a basic corpus of an application goes to the dictionary generation unit  36 . The dictionary generation unit  36  scans a basic dictionary  42  which has letter to sound rules for pronunciation and tokenization rules for decomposing syllables into phonetic sounds. The words from the basic corpus, with the applicable pronunciation rules, are relayed to the initial dictionary  32 , which may be directly processed into the pronunciation tuning unit  38 . The dictionary generation unit  36  collects the words and basic pronunciations from the basic dictionary  42 . The dictionary generation unit  36  may also collect sets of related accents, pronunciations and phonetic sounds from user profiles  46  and accent composition  44 . Together, these pronunciations gathered by the dictionary generation unit  36  form the initial dictionary  32  that is the training data  37  for the neural network  34 .  
         [0016]    The dictionary generation unit  36  has access to the basic dictionary  42  of common words, letter to sound rules for phonetics, and tokenization rules for partitioning words into smaller units of sound. The dictionary generation unit  36  accesses words from an application and creates the initial dictionary  32 . The initial dictionary  32  acts as a repository for the best pronunciations arrived at by the dictionary generation unit  36 . The initial dictionary  32  has access to a machine learning unit  40  with a neural network  34  that remembers alternative pronunciations for different letter combinations and can apply them to novel input scenarios. The dictionary generation unit  36  also accesses the accent composition  44  of various user profiles  46 . The accent composition  44  of actual user profiles  44  is stored so that the dictionary generation unit  36  may recognize the specific accents of users and generate the initial dictionary  32  according to the accent composition  44  and the basic dictionary  42 . In order to implement the accent composition  44 , previous user speech requests are recorded and matched to the current user in order to determine if a user profile  46  exists for the current user. The initial dictionary  32  relays this input from the dictionary generation unit  36  to the pronunciation tuning unit  38  and the machine learning unit  40 .  
         [0017]    The machine learning unit  40  contains the neural network  34  that calibrates differences between the pronunciation of specific words to reduce mapping errors. The machine learning unit  40  has the ability to learn new refinements (such as the accent composition  44  of users) which can increase subsequent efficiency. The pronunciation tuning unit  38  uses the machine learning unit  40  to refine the pronunciation of words from the initial dictionary  32 , and transmits the decoded words to the final pronunciation dictionary  41 . The pronunciation tuning unit  38  adds some alternative pronunciations for the application corpus. The final pronunciation dictionary  41  is a repository for the preferred selected alternatives of possible pronunciations for a particular word from the application corpus.  
         [0018]    For example, if the word “HOME” occurs in an application, the dictionary generation unit  36  checks the basic dictionary  42  for letter to sound rules to use as possibilities for pronouncing “HOME.” Possibilities for pronouncing “HO” of “HOME” might come from the words “HOW,” “HOLE,” or “HOOP.” These possibilities are relayed to the initial dictionary  32  from which the machine learning unit  40  and the pronunciation tuning unit  38  determine the most likely pronunciation. If the neural network  34  has encountered variations of “HO” before and changed “OW” after “H” to a long “O,” the new combination of letters in “HOME” will be facilitated by that experience in machine learning.  
         [0019]    [0019]FIG. 3 depicts an exemplary structure of the neural network  34 . The neural network  34  includes an input layer  70 , one or more hidden layers  72 , and an output layer  74 . The input layer  70  includes input nodes for the letter to be processed, left-context receptors and right-context receptors. The number of receptors to the right and left of the letter to be processed can be determined by the user, or may be determined by the network  34  based on, for example, the complexity of the language or the length of the word. In this exemplary structure, the neural network  34  includes a two letter bias for the right receptor and the left receptor. Alternatively, for shorter words, a one letter bias may be used for the right receptor and the left receptor.  
         [0020]    For example for the word “HOME”, the neural network  34  has the right-context receptor accept as input the letter “O” when it is processing the letter “H” and a null left text receptor. When the neural network  34  is processing the letter “O”, the left-context receptor accepts as input the letter “H” and the right-context receptor accepts as input the letter “M”. The neural network  34  continues to analyze each letter in the word in this manner until the last letter has been processed.  
         [0021]    Accordingly, the input size for the neural network  34  is the sum of the sizes of the left receptors, right receptors and the processed letter receptor. The values of each of the receptors is then generated according to the letter that is associated with that receptor.  
         [0022]    The hidden layers  72  process the input data based upon how the hidden layers&#39; weights and activation functions are trained. The present invention may use any type of activation function that suits the application at hand, such as a sigmoid squashing function. The output layer  74  generates phonemes based upon the input spelling. In one embodiment of the present invention the phonemes are binary encoded in order to generate more accurate and efficient representations. The ultimate mapping of the input spelled word to a set of phonemes by the neural network  34  is termed a pronunciation rule.  
         [0023]    It should be understood that various neural network structures may be utilized by the present invention. For example, the input layer to the neural network may have twenty ( 20 ) input nodes to process the letter and the left and right letters; or the neural network may have as many input nodes to simultaneously process all letters of the word. In this latter embodiment, the number of input nodes corresponds to the number of letters in the word to be processed. The hidden layers  72  determine phoneme pronunciation guides based upon each letter and the letter&#39;s left and right neighbors.  
         [0024]    [0024]FIG. 4 depicts as an exemplary operational scenario of the present invention wherein the word to be voiced contains the word “HOME”. Start block  100  indicates that process block  102  receives the word “HOME”  104 . Process block  106  performs a dictionary lookup from the basic dictionary and obtains the pronunciation /HH OW M/ in step  108 . This pronunciation is put in the initial dictionary. At process block  112 , the pronunciation tuning unit processes the dictionary lookup through the initial dictionary, thereby yielding a few more “alternative” pronunciations:  
         [0025]    HOME/HH OW M/  
         [0026]    /HH AX L M/  
         [0027]    /HH AX UH M/  
         [0028]    The pronunciation tuning unit also uses the neural network of the present invention to fine tune the pronunciations. If the neural network has the experience of changing “HO” from /HH OW/ to/HH AX L/, the new combination of letters “HOME” are added at process block  116  to the final pronunciation rules in addition to the other determined pronunciation rules.  
         [0029]    The preferred embodiment described within this document with reference to the drawing figures is presented only to demonstrate an example of the invention. Additional and/or alternative embodiments of the invention will be apparent to one of ordinary skill in the art upon reading this disclosure.