Patent Publication Number: US-2005131687-A1

Title: Portable wire-less communication device

Description:
This application claims the right of priority under 35 USC Section 119 based on UK Patent Application Numbers 0322516.6 filed 25 Sep. 2003, and 0408536.1 filed 16 Apr. 2004, which are hereby incorporated by reference herein in their entirety as if fully set forth herein.  
      The present invention relates to portable wire-less communication devices, such as cellular telephones, and in particular to the generation of text using such devices for use, for example, in text messages.  
      The short Messaging Service (SMS) allows text messages to be sent and received on cellular telephones. The text message can comprise words or numbers and is generated using a text editor module on the cellular telephone. SMS was created as part of the GSM Phase One standard and allows for up to one hundred and sixty characters to be transmitted in a single message.  
      When creating a message, the user enters the characters for the message via a keyboard associated with the cellular telephone. Typically, the keyboard on the cellular telephones has ten keys corresponding to the ten digits “0” to “9” and further keys for controlling the operation of the telephone such as “place call”, “end call” etc. To facilitate entry of letters and punctuation, for example, when composing a text message, the characters of the alphabet are divided into subsets and each subset is mapped to a different key of the keyboard. As there is not a one to one mapping between the characters of the alphabet and the keys of the keyboard, the keyboard can be said to be an “ambiguous keyboard”.  
      The text editor on the cellular telephone must therefore have some mechanism to disambiguate between the different letters associated with the same key. For example, in mobile telephones typically employed in Europe, the key corresponding to the digit “2” is also associated with the characters “A”, “B” and “C”. The two well known techniques for disambiguating letters typed on such an ambiguous keyboard are known as “multi-tap”, and “predictive text”. In the multi-tap” system, the user presses each key a number of times depending on the letter that the user wants to enter. For the above example, pressing the key corresponding to the digit “2” once gives the character “A”, pressing the key twice gives the character “B”, and pressing the key three times gives the character “C”. Usually there is a predetermined amount of time within which the multiple key strokes must be entered. This allows for the key to be re-used for another letter when necessary.  
      When using a cellular telephone having a predictive text editor, the user enters a word by pressing the keys corresponding to each letter of the word exactly once and the text editor includes a dictionary which defines the words which may correspond to the sequence of key presses. For example, if the keyboard contains (like most cellular telephones) the keys “ ”, “ABC”, “DEF”, “GHI”, “JKL”, “MNO”, “PQRS”, “TUV” and “WXYZ” and the user wants to enter the word “hello”, then he does this by pressing the keys “GHI”, “DEF”, “TKL”, “JKL”, “MNO” and “ ”. The predictive text editor then uses the stored dictionary to disambiguate the sequence of keys pressed by the user into possible words. The dictionary also includes frequency of use statistics associated with each word which allows the predictive text editor to choose the most likely word corresponding to the sequence of keys. If the predicted word is wrong then the user can scroll through a menu of possible words to select the correct word.  
      Cellular telephones having predictive text editors are becoming more popular because they reduce the number of key presses required to enter a given word compared to those that use multi-tap text editors. However, one of the problems with predictive text editors is that there are a large number of short words which map to the same key sequence. A dedicated key must, therefore be provided on the keyboard for allowing the user to scroll through the list of matching words corresponding to the key presses, if the predictive text editor does not predict the correct word.  
      It is an aim of the present invention to increase the speed and ease of generating text messages on a cellular communications device having an ambiguous keyboard.  
      In one aspect, the present invention provides a cellular telephone having a text editor for generating text messages for transmission to other users. The cellular telephone also includes a speech recognition circuit which can perform speech recognition on input speech and which can provide a recognition result to the text editor for display to the user on a display of the cellular telephone. In this way, the text editor can generate text for display either from key-presses input by the user on a keypad of the telephone or in response to a recognition result generated by the speech recognition circuit.  
      In another aspect, the present invention provides a cellular device having speech recognition means for performing speech recognition on a speech sample containing a word the user desires to be entered into a text editor, the speech recognition means having a grammar that is constrained in accordance with previous key presses made by the user. 
    
    
      Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which:  
       FIG. 1  shows a cellular telephone having an ambiguous keyboard for both number and letter entry;  
       FIG. 2  is a block diagram illustrating the main functional components of a text editor which forms part of the cellular telephone shown in  FIG. 1 ;  
       FIG. 3  is a flowchart illustrating the main processing steps performed by a keyboard processor shown in  FIG. 2  in response to receiving a keystroke input from the cellular telephone keyboard;  
       FIG. 4  is a table illustrating part of the data used to generate a predictive text graph and a word dictionary shown in  FIG. 2 ;  
       FIG. 5   a  schematically illustrates part of a predictive text graph generated from the data in the table shown in  FIG. 4 ;  
       FIG. 5   b  illustrates the predictive text graph shown in  FIG. 5   a  in tabular form;  
       FIG. 6   a  illustrates part of an ASR grammar defined with context independent phonemes;  
       FIG. 6   b  illustrates a portion of a grammar used by an automatic speech recognition circuit which forms part of IS the text editor shown in  FIG. 2 ;  
       FIG. 7  is a table illustrating the form of the word dictionary shown in  FIG. 2 ;  
       FIG. 8   a  is a flowchart illustrating the processing steps performed by a control unit shown in  FIG. 2 ;  
       FIG. 8   b  is a flowchart illustrating the processing steps performed by the control unit when the control unit receives an input from a keyboard processor shown in  FIG. 2 ;  
       FIG. 8   c  is a flowchart illustrating the processing steps performed by the control unit upon receipt of a confirmation signal;  
       FIG. 8   d  is a flowchart illustrating the processing steps performed by the control unit upon receipt of a cancel signal;  
       FIG. 8   e  is a flowchart illustrating the processing steps performed by the control unit upon receipt of a shift signal;  
       FIG. 8   f  is a flowchart illustrating the processing steps performed by the control unit upon receipt of a text key signal;  
       FIG. 8   g  is a flowchart illustrating the processing steps performed by the control unit when the control unit receives an input from a speech input button shown in  FIG. 2 ; and  
       FIG. 9  is a block diagram illustrating the functional blocks of a system used to generate the predictive text graph and the word dictionary used by the text editor shown in  FIG. 2 .  
    
    
     OVERVIEW  
       FIG. 1  illustrates a cellular telephone  1  having a text editor (not shown) embodying the present invention. The cellular telephone  1  includes a display  5 , a speaker  7  and a microphone  9 . The cellular telephone  1  also has an ambiguous keyboard  2 , including keys  3 - 1  to  3 - 10  for entry of letters and numbers and keys  3 - 1  to  3 - 17  for controlling the operation of the cellular telephone  1 , as defined in the following table:  
                                                           KEY   NUMBER   LETTERS   FUNCTION                          3-1   1   —   Punctuation           3-2   2   abc   —           3-3   3   def   —           3-4   4   ghi   —           3-5   5   jkl   —           3-6   6   mno   —           3-7   7   pqrs   —           3-8   8   tuv   —           3-9   9   wxyz   —           3-10   0   —   space           3-11   —   —   spell           3-12   —   —   caps           3-13   —   —   confirm           3-14   —   —   cancel           3-15   —   —   shift           3-16   —   —   send/make call           3-17   —   —   END CALL                        
      The telephone  1  also includes a speech input button  4  for informing the telephone  1  when control speech is being or is about to be entered by the user via the microphone  9 .  
      The text editor can operate in a conventional manner using predictive text. However, in this embodiment the text editor also includes an automatic speech recognition unit (not shown), which allows the text editor to be able to use the user&#39;s speech to disambiguate key strokes made by the user on the ambiguous keyboard  2  and to reduce the number of key strokes that the user has to make to enter a word into the text editor. In operation, the text editor uses key strokes input by the user to confine the recognition vocabulary used by the automatic speech recognition unit to decode the user&#39;s speech. The text editor then displays the recognized word on the display  5  thereby allowing the user to accept or reject the recognized word. If the user rejects the recognized word by typing further letters of the desired word, then the text editor can re-perform the recognition, using the additional key presses to further limit the vocabulary of the speech recognition unit In the worst case, therefore, the text editor will operate as well as a conventional text editor, but in most cases the use of the speech information will allow the correct word to be identified much earlier (i.e. with less keystrokes) than with a conventional text editor.  
      Text Editor  
       FIG. 2  is a schematic block diagram showing the main components of the text editor  11  used in this embodiment. As shown, the text editor  11  includes a keyboard processor  13  which receives an ID signal from the keyboard  2  each time the user presses a key  3  on the keyboard  2 , which ID signal identifies the particular key  3  pressed by the user. The received key ID and data representative of the sequence of key presses that the user has previously entered since the last end of word identifier (usually identified by the user pressing the space key  3 - 10 ) is then used to address a predictive text graph  17  to determine data identifying the most likely word that the user wishes to input The data representative of the sequence of key presses that the user has previously entered is stored in a key register  14 , and is updated with the most recent key press after it has been used to address the predictive text graph  17   
      The keyboard processor  13  then passes the data identifying the most likely word to the control unit  19  which uses the data to determine the text for the predicted word from a word dictionary  20 . The control unit  19  then stores the text for the predicted word in an internal memory (not shown) and then outputs the text for the predicted word on the display  5 . In this embodiment the stem of the predicted word (defined as being the first i letters of the word, where i is the number of key presses made by the user when entering the current word on the keyboard  2 ) is displayed in bold text and the remainder of the predicted word is displayed in normal text. This is illustrated in  FIG. 1  for the current predicted word “abstract” after the user has pressed the key sequence “22”  FIG. 1  also shows that, in this embodiment, the cursor  10  is positioned at the end of the stem  12 .  
      In this embodiment, when the key ID for the latest key press and the data representative of previous key presses is used to address the predictive text graph  17 , this also gives data identifying all possible words known to the text editor  11  that correspond to the key sequence entered by the user. The keyboard processor  13  passes this “possible word data” to an activation unit  21  which uses the data to constrain the words that the automatic speech recognition (ASR) unit  23  can recognize. In this embodiment, the ASR unit  23  is arranged to be able to discriminate between several thousand words pronounced in isolation. Since computational resources (both processing power and memory) on a cellular telephone  1  are limited, the ASR unit  23  compares the input speech with phoneme based models  25  and the allowed sequences of the phoneme based models  25  are constrained to define the allowed words by an ASR grammar  27 . Therefore, in this embodiment, the activation unit  21  uses the possible word data to identify, from the word dictionary  20 , the corresponding portions of the ASR grammar  27  to be activated.  
      If the user then presses the speech button  4 , the control unit  19  is informed that speech is about to be input via the microphone  9  into a speech buffer  29 . The control unit  19  then activates the ASR unit  23  which retrieves the speech from the speech buffer  29  and compares it with the appropriate phoneme based models  25  defined by the activated portions of the ASR grammar  27 . In this way, the ASR unit  23  is constrained to compare the input speech only with the sequences of phoneme based models  25  that define the possible words identified by the keyboard processor  13 , thereby reducing the processing burden and increasing the recognition accuracy of the ASR unit  23 .  
      The ASR unit  23  then passes the recognized word to the control unit  19  which stores and displays the recognized word on the display  5  to the user. The user can then accept the recognized word by pressing the accept or confirmation key  3 - 13  on the keyboard  2 . Alternatively, the user can reject the recognized word by pressing the key  3  corresponding to the next letter of the word that they wish to enter. In response, the keyboard processor  13  uses the entered key, the data representative of the previous key presses for the current word and the predictive text graph  17  to update the predicted word and outputs the data identifying the updated predicted word to the control unit  19  as before. The keyboard processor  13  also passes the data identifying the updated list of possible words to the activation unit  21  which reconstrains the ASR grammar  27  as before. In this embodiment, when the control unit  19  receives the data identifying the updated predicted word from the keyboard processor  13 , it does not use it to update the display  5 , since there is speech for the current word being entered in the speech buffer  29 . The control unit  19 , therefore, re-activates the ASR unit  23  to reprocess the speech stored in the speech buffer  29  to generate a new recognised word. The ASR unit  23  then passes the new recognised word to the control unit  19  which displays the new recognised word to the user on the display  5 . This process is repeated until the user accepts the recognized word or until the user has finished typing the word on the keyboard  2 .  
      A brief description has been given above of the operation of the text editor  11  used in this embodiment. A more detailed description will now be given of the operation of the main units in the text editor  11  shown in  FIG. 2 .  
      Keyboard Processor  
       FIG. 3  is flow chart illustrating the operation of the keyboard processor  13  used in this embodiment. As shown, at step s 1 , the keyboard processor  13  checks to see if a key  3  on the keyboard  2  has been pressed by the user. When a key press is detected, the processing proceeds to step s 3  where the keyboard processor  13  checks to see if the user has just pressed the confirmation key  3 - 13  (by comparing the received key ID with the key ID associated with the confirmation key  3 - 13 ). If he has then, at step s 5 , the keyboard processor  13  sends a confirmation signal to the control unit  19  and then resets the activation unit  21  and its internal register  14  so that they are ready for the next series of key presses to be input by the user for the next word. The processing then returns to step s 1 .  
      If the keyboard processor  13  determines at step s 3  that the confirmation key  3 - 13  was not pressed, then the processing proceeds to step s 7  where the keyboard processor  13  determines if the cancel key  3 - 14  has just been pressed. If it has, then the keyboard processor  13  proceeds to step s 9  where it sends a cancel signal to the control unit  19  so that the current predicted or recognised word is removed from the display S and so that the speech can be deleted from the buffer  29 . In step s 9  the keyboard processor  13  also resets the activation unit  21  and its internal register  14  so that they are ready for the. next word to be entered by the user. The processing then returns to step s 1 .  
      If at step s 7 , the keyboard processor  13  determines that the cancel key  3 - 14  was not pressed then the processing proceeds to step s 11  where the keyboard processor  13  determines whether or not the shift key  3 - 15  has just been pressed. If it has, then the processing proceeds to step s 13  where the keyboard processor  13  sends a shift control signal to the control unit  19  which causes the control unit  19  to move the cursor  10  one character to the right along the predicted or recognised word. The control unit  19  then identifies the letter following the current position of the cursor  10  on the displayed predicted or recognized word. For example, if the user presses the shift key  3 - 15  for the displayed message shown in  FIG. 1 , then the control unit  19  will identify the letter “s” of the currently displayed word “abstract”. The control unit  19  then returns the identified letter to the keyboard processor  13  which uses the identified letter and the previous key press data stored in the key register  14  to update the data identifying the possible words corresponding to the updated key sequence, using the predictive text graph  17 . The keyboard processor  13  then passes the data identifying the updated possible words to the activation unit  21  as before. The processing then returns to step s 1 .  
      If at step s 11 , the keyboard processor  13  determines that the shift key  3 - 15  was not pressed, then the processing proceeds to step s 15 , where the keyboard processor  13  determines whether or not the space key  3 - 10  has just been pressed. If it has, then the keyboard processor  13  proceeds to step s 17 , where the keyboard processor  13  sends a space command to the control unit  19  so that it can update the display  5 . At step s 17 , the keyboard processor  13  also resets the activation unit  21  and its internal register  14 , so that they are ready for the next word to be entered by the user. The processing then returns to step s 1 .  
      If at step s 15 , the keyboard processor  13  determines that the space key  3 - 10  was not pressed, then the processing proceeds to step s 19  where the keyboard processor  13  determines whether or not a text key ( 3 - 2  to  3 - 9 ) has been pressed. If it has, then the processing proceeds to step s 21  where the keyboard processor  13  uses the key ID for the text key that has been pressed to update the predictive text and to inform the control unit  19  of the new key press and of the new predicted word. At step s 21 , the keyboard processor  13  also uses the latest text key  3  input to update the data identifying the possible words that correspond to the updated key sequence, which it passes to the activation unit  21  as before. The processing then returns to step s 1 .  
      If at step s 19 , the keyboard processor  13  determines that a text key ( 3 - 2  to  3 - 9 ) was not pressed then the processing proceeds to step s 23  where the keyboard processor  13  checks to see if the user has pressed a key to end the text message, such as the send message key  3 - 16 . If he has then the keyboard processor  13  informs the control unit  19  accordingly and then the processing ends. Otherwise the processing returns to step s 1 .  
      Although not discussed above, the keyboard processor  13  also has routines for dealing with the inputting of punctuation marks by the user via the key  3 - 1  and routines for dealing with left shifts and deletions etc. These routines are not discussed as they are not needed to understand the present invention.  
      Predictive Text  
      As discussed above, the keyboard processor  13  uses predictive text techniques to map the sequence of ambiguous key presses entered via the keyboard  2  into data that identities all possible words that can be entered by such a sequence. This is slightly different from existing predictive text systems which only determine the most likely word that corresponds to the entered key sequence. As discussed above, the keyboard processor  13  determines the data that identifies all of these words from the predictive text graph  17 .  FIG. 4  is a table illustrating part of the word data used to generate the predictive text graph  17  used in this embodiment. As those skilled in the art will appreciate, the predictive text graph  17  can be generated in advance from the data shown in  FIG. 4  and then downloaded into the telephone at an appropriate time.  
      As shown in  FIG. 4 , the word data includes w rows of word entries  50 - 1  to  50 -W, where W is the total number of words that will be known to the keyboard processor  13 . Each of the word entries  50  includes a key sequence portion  51  which identifies the sequence of key presses required by the user to enter the word via the keyboard  2  of the cellular telephone  1 . Each word entry  50  also has an associated index value  53  that is unique and which identifies the word corresponding to the word entry  50 , and the text  55  for the word entry so. For example, for the word “abstract”, this has the index value of “6” and is defined by the user pressing the following key sequence “22787228”. As shown in  FIG. 4 , the word entries  50  are arranged in the table in numerical order based on the sequence of key-presses rather than alphabetical order based on the letters of the words. The important property of this arrangement is that given a sequence of key-presses, all of the words that begin with that sequence of key-presses are consecutive in the table. This allows all of the possible words corresponding to an input sequence of key-presses to be identified by the index value  53  for the first matching word in the table and the total number of matching words. For example, if the user presses the “2” key  3 - 2  twice, then the list of possible words corresponds to the word “cab” through to the word “actions” and can be identified by the index value “2” and the range “8”.  
      Part of the predictive text graph  17  generated from the word data shown in  FIG. 4  is shown in a tree structure in  FIG. 5   a . As shown, the predictive text graph  17  includes a plurality of nodes  81 - 1  to  81 -M and a number of arcs, some of which are referenced  83 , which connect the nodes  81  together in a tree structure. Each of the nodes  81  in the predictive text graph  17  corresponds to a unique sequence of key presses and the arc extending from a parent node to a child node is labelled with the key ID for the key press required to progress from the parent node to the child node.  
      As shown in  FIG. 5   a , in this embodiment, each node  81  includes a node number N 1  which identifies the node  81 . Each node  81  also includes three integers (j, k, l), where j is the value of the word index  53  shown in  FIG. 4  for the first word in the table whose key sequence  51  starts with the sequence of key-presses associated with that node; k is the number of words in the table whose key sequence  51  starts with the sequence of key-presses associated with the node; and 1 is the value of the word index  53  of the most likely word for the sequence of key-presses associated with the node. As with conventional predictive text systems, the most likely word matching a given sequence of key-presses is determined in advance by measuring the frequency of occurrence of words in a large corpus of text.  
      As those skilled in the art will appreciate, the predictive text graph  17  shown in  FIG. 5   a  is not actually stored in the mobile telephone  1  in such a graphical way. Instead, the data represented by the nodes  81  and arcs  83  shown in  FIG. 5   a  are actually stored in a data array, like the table shown in  FIG. 5   b . As shown, the table includes M rows of node entries  90 - 1  to  90 -M, where M is the total number of nodes  81  in the text graph  17 . Each of the node entries  90  includes the node data for the corresponding node  81 . As shown, the data stored for each node includes the node number (N i )  91  and the j, k and l values  92 ,  93  and  94  respectively. Each of the node entries  90  also includes parent node data  97  that identifies its parent node. For example, the parent node for node N 2  is node N 1 . Each node entry  90  also includes child node data  99  which identifies the possible child nodes from the current node and the key press associated with the transition between the current node and the corresponding child node. For example, for node N 2 , the child node data  99  includes a pointer to node N 3  if the next key press entered by the user corresponds to the “2” key  3 - 2 ; a pointer to node N 12  if the next key press entered by the user corresponds to the “3” key  3 - 3 ; and a pointer to node N 23  if the next key press entered by the user corresponds to the “9” key  3 - 9 . Where there are no child nodes for a node, the child node data  99  for that node is left empty.  
      During use, the keyboard processor  13  stores the node number  91  identifying the sequence of key presses previously entered by the user for the current word, in the key register  14 . If the user then presses another one of the text input keys  3 - 2  to  3 - 9 , then the keyboard processor  13  uses the stored node number  91  to find the corresponding node entry  90  in the text graph  17 . The keyboard processor  13  then uses the key ID for the new key press to identify the corresponding child node from the child node data  99 . For example, if the user has previously entered the key sequence “22” then the node number  91  stored in the register  14  will be for node N 2 , and if the user then presses the “8” 0  key, then the keyboard processor  13  will identify (from the child node data  99  for node entry  90 - 3 ) that the child node for that key-press is node N 9 . The keyboard processor  13  then uses the identified child node number to find the corresponding node entry  90 , from which it reads out the values of j, k and l. For the above example, when the child node is N 9  the node entry is  90 - 9  and the value of j is 7 indicating that the first word that starts with the corresponding sequence of key-presses is the word “action”; the value of k is 3 indicating that there are only three words in the table shown in  FIG. 4  which start with this sequence of key-presses; and the value of l is 7, indicating that the most likely word that is being input given this sequence of key-presses is the word “action”.  
      After the keyboard processor  13  has determined the values of j, k and l, it updates the node number  91  stored in the key register  14  with the node number for the child node just identified (which in the above example is the node number  90 - 9  for node N 9 ) and outputs the j and k values to the activation unit  21  and the l value to the control unit  19 .  
      The activation unit  21  then uses the received values of j and k to access the word dictionary  20  to determine which portions of the ASR grammar  27  need to be activated. In this embodiment, the word dictionary  20  is formed as a table having the text  55  of all of the words shown in  FIG. 4  together with the corresponding index  53  for those words. The word dictionary  20  also includes, for each word, data identifying the portion of the ASR grammar  27  which corresponds to that word, which allows the activation unit  21  to be able to activate the portions of the ASR grammar  27  corresponding to the possible word data (identified by j and k). Similarly, the control unit  19  uses the received value of 1 to address the word dictionary  20  to retrieve the text  55  for the identified word predicted by the keyboard processor  13 . The control unit  19  also keeps track of how many key-presses have been made by the user so that it can control the position of the cursor  10  on the display  5  so that it appears at the end of the stem of the currently displayed word.  
      ASR Grammar  
      As discussed above, in this embodiment, the automatic speech recognition unit  23  recognises words in the input speech signal by comparing it with sequences of phoneme-based models  25  defined by the ASR grammar  27 . In this embodiment, the ASR grammar  27  is optimised into a “phoneme tree” in which phoneme models that belong to different words are shared among a number of words. This is illustrated in  FIG. 6   a  which shows how a phoneme tree  100  can define different words—in this case the words “action”, “actions”, “actionable” and “abstract”. As shown, the phoneme tree  100  is formed by a number of nodes  101 - 0  to  101 - 15 , each of which has a phoneme label that identifies the corresponding phoneme model. The nodes  101  are connected to other nodes  101  in the tree by a number of arcs  103 - 1  to  103 - 19 . Each branch of the phoneme tree  100  ends with a word node  105 - 1  to  105 - 4  which defines the word represented by the sequence of models along the branch from the initial root node  101 - 0  (representing silence). The phoneme tree  100  defines through the interconnected nodes  101 , which sequences of phoneme models the input speech is to be compared with. In order to reduce the amount of processing, the phoneme tree  100  shares the models used for words having a common root, such as for the words “action” and “actions”.  
      As those skilled in the art of speech recognition will appreciate, the use of such a phoneme tree  100  reduces the burden on the automatic speech recognition unit  23  to compare the input speech with the phoneme based models  25  for all the words in the ASR vocabulary. However, in order to obtain good accuracy, context dependent phoneme-based models  25  are preferably used. In particular, during normal speech, the way in which a phoneme is pronounced depends on the phonemes spoken before and after that phoneme. The use of “tri-phone” models which store a model for sequences of three phonemes are often used. However, the use of such tri-phone models reduces the optimisation achieved in using the phoneme tree shown in  FIG. 6   a . In particular, if tri-phone models are used then the model for “n” in the word “action” could not be shared with the model for “n” in the words “actions” and “actionable”. In fact there would need to be three different tri-phone models: “sh−n+sil”, “sh−n+z” and “sh−n+ax” (where the notation x−y+z means that the phone y has left context x and right context z). However, since in a tree structure every node  101  (corresponding to a phoneme model) has exactly one parent node, the left context can always be preserved. For the nodes with only one child, also the right context can be preserved. For nodes that have more than one child, bi-phone models are used with specified left context and open (unspecified) right context. The final phoneme tree  100  for the words shown in  FIG. 6   a  is shown in  FIG. 6   b . As illustrated, each of the nodes  101  includes a phoneme label which identifies the corresponding tri-phone or bi-phone model stored in the phoneme-based models  25 .  
      As discussed above, the list of words recognisable by the automatic speech recognition unit  23  varies depending on the output of the keyboard processor  13 . Any word recognised by the automatic speech recognition unit  23  must in fact satisfy the constraints imposed by the sequence of keys entered by the user As discussed above, this is achieved by the activation unit  21  controlling which portions of the ASR grammar  27  are active and therefore used in the recognition process. This is achieved, in this embodiment, by the activation unit  21  activating the appropriate arcs  103  in the ASR grammar  27  for the possible words identified by the keyboard processor  13 . In this embodiment, the identifiers for the arcs  103  associated with each word are stored within the word dictionary  20  so that the activation unit  21  can retrieve and can activate the appropriate arcs  103  without having to search for them in the ASR grammar  27 .  
       FIG. 7  is a table illustrating the content of the word dictionary  20  used in this embodiment. As shown, the word dictionary  20  includes the index  53  and the word text  55  of the table shown in  FIG. 4 . The word dictionary  20  also includes arc data  57  identifying the arcs  103  for the corresponding word in the ASR grammar  27 . For example, for the word “action”, the arcs data  57  includes arcs  103 - 1  to  103 - 5 . The activation unit  21  can therefore identify the relevant arcs  103  to be activated using the j and k values received from the keyboard processor  13  to look up the corresponding arc data  57  in the word dictionary  20 . In particular, the activation unit uses the value of j received from the keyboard processor  13  to identify the first word in the word dictionary  20  that may correspond to the input sequence of key presses. The activation unit  21  then uses the k value received from the keyboard processor  13  to select the k words in the word dictionary (starting from the first word identified using the received j value). The activation unit  21  then reads out the arc data  57  from the selected words and uses that arc data  57  to activate the corresponding arcs in the ASR grammar  27 .  
       FIG. 6   b  illustrates the selective activation of the arcs  103  by the activation unit  21 , when the arcs  103 - 1  to  103 - 11  for the. words “action”, “actions” and “actionable” are activated and the arcs  101 - 12  to  101 - 19  associated with the word “abstract” are not activated and are shown in phantom.  
      Control Unit  
       FIG. 8 , comprising  FIGS. 8   a  to  8   g  are flowcharts illustrating the operation of the control unit  19  used in this embodiment. As shown in  FIG. 8   a , the control unit  19  continuously checks in steps s 31  and s 33  whether or not it has received an input from the keyboard processor  13  or if the speech button  4  has been pressed. If the control unit detects that it has received an input from the keyboard processor  13 , then the processing proceeds to “A” shown at the top of  FIG. 8   b , otherwise if the control unit  19  determines that the speech input button  4  has been pressed then it proceeds to “B” shown at the top of  FIG. 8   g.    
      As shown in  FIG. 8   b , if the control unit detects that it has received an input from the keyboard processor  13 , then the processing proceeds to step s 41  where the control unit determines whether or not it has received a confirmation signal from the keyboard processor  13 . If it has received a confirmation signal, then the processing proceeds to “C” shown in  FIG. 8   c , where the control unit  19  updates the display  5  to confirm the currently displayed candidate word. The processing then proceeds to step s 53  where the control unit resets a “speech available flag” to false, indicating that speech is no longer available for processing by the ASR unit  23 . The processing then proceeds to step s 55  where the control unit  19  resets any predictive text candidate stored in its internal memory. The processing then returns to step s 31  shown in  FIG. 8   a.    
      If at step s 41 , the control unit  19  determines that a confirmation signal was not received, then the processing proceeds to step s 43  where the control unit  19  checks to see if a cancel signal has been received. If it has, then the processing proceeds to “D” shown in  FIG. 8   d  As shown, in this case, the control unit  19  resets, in step s 61 , the speech available flag to false and then, in step s 63 , resets the predictive text candidate by deleting it from its internal memory. The control unit  19  then updates the display  5  to remove the current predicted word being entered by the user. The processing then returns to step s 31  shown in  FIG. 8   a.    
      If at step s 43 , the control unit determines that a cancel signal has not been received, then at step s 45 , the control unit determines whether or not it has received a shift signal. If it has, then the processing proceeds to “E” shown in FIG. Be As shown, at step s 71 , the control unit  19  identifies the letter following the current cursor position. The processing then proceeds to step s 73  where the control unit  19  returns the identified letter to the keyboard processor  13 , so that the keyboard processor  13  can update its predictive text routine. The processing then proceeds to step s 75  where the control unit  19  updates the cursor position on the display  5  by moving the cursor  10  one character to the right. The processing then returns to step s 31  shown in  FIG. 8   a.    
      If at step s 45 , the control unit  19  determines that a shift signal has not been received, then the processing proceeds to step s 47  where the control unit  19  determines whether or not it has received a text key and a predictive text candidate from the keyboard processor  13 . If it has, then the processing proceeds to “F” shown at the top of  FIG. 8   f . As shown, in this case, at step  981 , the control unit  19  determines whether or not speech is available in the speech buffer  29  (from the status of the “speech available flag”). If speech is available, then the processing proceeds to step s 83  where the control unit  19  discards the current ASR candidate and then, in step s 85 , instructs the ASR unit  23  to re-perform the automatic speech recognition on the speech stored in the speech buffer  29 . In this way, the speech recognition unit  23  will re-perform the speech recognition in light of the updated predictive text generated by the keyboard processor  13 . The processing then proceeds to step s 87  where the control unit  19  determines whether or not a new ASR candidate is available. If it is, then the processing proceeds to step s 89  where the new ASR candidate is displayed on the display  5 . The processing then returns to step s 31  shown in  FIG. 8   a . If, at step s 81  the control unit  19  determines that speech is not available or if at step s 87  the control unit  19  determines that an ASR candidate is not available, then the processing proceeds to step s 91  where the control unit  19  uses the predictive text data (the value of the integer 1) received from the keyboard processor  13  to retrieve the corresponding text  55  from the word dictionary  20 . The processing then proceeds to step s 93  where the control unit  19  displays the predictive text candidate on the display S The processing then returns to step s 31  shown in  FIG. 8   a.    
      If at step s 47 , the control unit  19  determines that a text key and predictive text candidate have not been received from the keyboard processor, then the processing proceeds to step s 49  where the control unit  19  determines whether or not an end text message signal has been received. If it has, then the processing ends, otherwise, the processing returns to step s 31  shown in  FIG. 8   a.    
      Although not shown in  FIG. 8 , the control unit  19  will also have routines for dealing with the inputting of punctuation marks, the shifting of the cursor to the left and the deletion of characters from the displayed word. Again, these routines are not shown because they are not relevant to understanding the present invention.  
      If at step s 33 , the control unit  19  determines that the speech input button  4  has been pressed, then the processing proceeds to “B” shown at the top of  FIG. 8   g . As shown, in step S 100 , the control unit  19  initially resets the speech available flag to false so that previously entered speech stored in the speech buffer  29  is not processed by the ASR unit  23 . In steps S 101  and S 103 , the control unit prompts the user to input speech and waits until new speech has been entered. Once speech has been input by the user and the speech available flag has been set, the processing proceeds to step s 105  where the control unit  19  instructs the ASR unit  23  to perform speech recognition on the speech stored in the speech buffer  29 . The processing then proceeds to step s 107  where the control unit  19  checks to see if an ASR candidate word is available. If it is, then the processing proceeds to step s 109  where the control unit  19  displays the ASR candidate word on the display  5 . The processing then returns to step s 31  shown in  FIG. 8   a . If, however, an ASR candidate word is not available at step s 107 , then the processing proceeds to step sill where the control unit  19  checks to see if at least one text key  3  has been pressed. If the user has not made any key presses, then the processing proceeds to step s 115  where the control unit  19  displays no candidate word on the display  5  and the processing then returns to step s 31  shown in  FIG. 8a . If, however, the control unit  19  determines at step s 111  that the user has pressed one or more keys  3  on the keyboard  2 , then the processing proceeds to step s 113  where the control unit  19  displays the predicted candidate word identified by the keyboard processor  13 . The processing then returns to step s 31  shown in  FIG. 8   a.    
      A detailed description of a cellular telephone  1  embodying the present invention has been given above. As described, the cellular telephone  1  includes a text editor  11  that allows users to input text messages into the cellular telephone  1  using a combination of voice and typed input. Where keystrokes have been entered into the telephone  1 , the automatic speech recognition unit  23  was constrained in accordance with the keystrokes entered. Depending on the number of keystrokes entered, this can significantly increase the recognition accuracy and reduce recognition time. To achieve this, in the above embodiment, the predictive text graph included data identifying all words which may correspond to any given sequence of input characters and a word dictionary was provided which identified the portions of the ASR grammar  27  that were to be activated for a given sequence of key presses. As discussed above, this data is calculated in advance and then stored or downloaded into the cellular telephone  1 .  
       FIG. 9  is a block diagram illustrating the main components used to generate the word dictionary  20  and the predictive text graph  17  used in this embodiment. As shown, these data structures are generated from two base data sources—dictionary data  123  which identifies all the words that will be known to the keyboard processor  13  and to the ASR unit  23 ; and keyboard layout data  125  which defines the relationship between key presses and alphabetical characters. As shown in  FIG. 9 , the dictionary data  123  is input to an ASR grammar generator  127  which generates the ASR grammar  27  discussed above. The dictionary data  123  is also input to a word-to-key mapping unit  129  which uses the keyboard layout data  125  to determine the sequence of key presses required to input each word defined by the dictionary data  123  (i.e. the key sequence data  51  shown in  FIG. 4 ). Since the dictionary data  123  will usually store the words in alphabetical order, the words and the corresponding key sequence data  51  generated by the word-to-key mapping unit  129  is likely to be in alphabetical order. This word data and key sequence data  51  is then sorted by a sorting unit  131  into numerical order based on the sequence of key presses required to input the corresponding word. The sorted list of words and the corresponding key presses is then output to a word dictionary generator  133  which generates the word dictionary  20  shown in  FIG. 7 . The sorted list of words and corresponding key presses is also output to a predictive text generator  135  which generates the predictive text graph  17  shown in  FIG. 8   b.    
      Modifications and Alternatives  
      In the above embodiment, a cellular telephone was described which included a predictive text keyboard processor which operated to predict words being input by the user. The key presses entered by the user were also used to constrain the recognition vocabulary used by an automatic speech recognition unit In an alternative embodiment, the text editor may include a conventional “multi-tap” keyboard processor in which text prediction is not carried out. In such an embodiment, the confirmed letters entered by the user can still be used to constrain the ASR vocabulary used during a recognition operation. In such an embodiment, because letters are being confirmed by the keyboard processor, the data stored in the word dictionary is preferably sorted alphabetically so that the relevant words to be activated in the ASR grammar again appear consecutively in the word dictionary.  
      In the above embodiment, the predictive text graph included, for each node in the graph, not only data identifying the predicted word corresponding to the sequence of key presses, but also data identifying the first word in the word dictionary that corresponds to the sequence of key presses and the number of words within the dictionary that correspond to the sequence of key presses. The activation unit used this data to determine which arcs within the ASR grammar should be activated for the recognition process. As those skilled in the art will appreciate, it is not essential for the keyboard processor to identify the first word within the word dictionary which corresponds to the sequence of key presses. Indeed, it is not essential to store the “j” and “k” data in each node of the predictive text graph. Instead, the keyboard processor may simply identify the most likely word to the activation unit, provided the data stored in the word dictionary for that most likely word includes the arcs for all words corresponding to that input key sequence. For example, referring to  FIG. 4 , if the input key sequence corresponds to “228” and the most likely word is the word “action”, then provided the arc data stored in the word dictionary for the word “action” includes the arcs within the ASR grammar for the words actionable and actions, then the activation unit can still activate the relevant portions of the ASR grammar.  
      In the above embodiment, the text editor was arranged to display the full word predicted by the keyboard processor or the ASR candidate word for confirmation by the user. In an alternative embodiment, only the stem of the predicted or ASR candidate word may be displayed to the user. However, this is not preferred, since the user will still have to make further key-presses to enter the correct word.  
      In the above embodiment, the text editor included an embedded automatic speech recognition unit. As those skilled in the art will appreciate, this is not essential. The automatic speech recognition unit may be provided separately from the text editor and the text editor may simply communicate commands to the separate automatic speech recognition unit to perform the recognition processing.  
      In the above embodiment, the word dictionary data and the predictive text graph were stored in two separate data stores. As those skilled in the art will appreciate, a single data structure may be provided containing both the predictive text graph data and the word dictionary data.  
      In such an embodiment, the keyboard processor, the activation unit and the control unit would then access the same data structure  
      In the above embodiment, the automatic speech recognition unit stored a word grammar and phoneme-based models. As those skilled in the art will appreciate, it is not essential for the ASR unit to be a phoneme-based device. For example, the ASR unit may be a word-based automatic speech recognition unit. In this case, however, if the ASR dictionary is to be the same size as the dictionary for the keyboard processor then this will require a substantial memory to store all of the word models. Further, in such an embodiment, the control unit may be arranged to limit the operation of the ASR unit so that speech recognition is only performed provided the possible words corresponding to the sequence of key-presses is below a predetermined number of words. This will speed up the recognition processing an devices having limited memory and/or processing power.  
      In the above embodiment, the automatic speech recognition unit used the same grammar (i.e. dictionary words) as the keyboard processor. As those skilled in the art will appreciate, this is not essential. The keyboard processor or the ASR unit may have a larger vocabulary than the other.  
      In the above embodiment, when displaying a predicted or  
      ASR candidate word to the user, the control unit placed the cursor at the end of the stem of the displayed word allowing the user to either confirm the word or to press the shift key to accept letters in the displayed word. As those skilled in the art will appreciate, this is not the only way that the control unit can display the candidate word to the user. For example, the control unit may be arranged to display the whole predicted or candidate word and place the cursor at the end of the word. The user can then accept the predicted or candidate word simply by pressing the space key. Alternatively, the user can use a left-shift key to go back and effectively reject the predicted or candidate word. In such an embodiment, the ASR unit may be arranged to re-perform the recognition processing excluding the rejected candidate word.  
      In the above embodiment, the control unit only displayed the most likely word corresponding to the ambiguous set of input key presses. In an alternative embodiment, the control unit may be arranged to display a list of candidate words (for example in a pop-up list) which the user can then scroll through to select the correct word.  
      In the above embodiment, when the user rejects an automatic speech recognition candidate word by, for example, typing the next letter of the desired word, the control unit caused the ASR unit to re-perform the speech recognition processing. Additionally, as those skilled in the art will appreciate, the control unit can also inform the activation unit that the previous ASR candidate word was not the correct word and that therefore, the corresponding arcs for that word should not be activated when taking into account the new key press. This will ensure that the automatic speech recognition unit will not output the same candidate word to the control unit when re-performing the recognition processing.  
      Although not described in the above embodiment, the text editor will also allow users to be able to “switch off” the predictive text nature of the keyboard processor. This will allow users to be able to use the multi-tap technique to type in words that may not be in the dictionary.  
      In the above embodiment, the predictive text graph, the word dictionary and the ASR grammar were downloaded and stored in the cellular telephone in advance of use by the user As those skilled in the art will appreciate, it is possible to allow the user to update or to add words to the predictive text graph, the word dictionary and/or the ASR grammar. This updating may be done by the user entering the appropriate data via the keypad or by downloading the update data from an appropriate service provider.  
      In the above embodiment, if the automatic speech recognition unit did not recognise the correct word, then the controller can instruct the ASR unit to re-perform the recognition processing after the user has typed in one or more further letters of the desired word. Alternatively, if the ASR unit determines that the quality of the input speech is insufficient, it can inform the control unit which can then prompt the user to input the speech again.  
      In the above embodiment, the list of arcs for a word within the ASR grammar were stored within the word dictionary and the activation unit used the arc data to activate only those arcs for the possible words identified by the keyboard processor. As those skilled in the art will appreciate, this is not essential. The keyboard processor may simply inform the activation unit of the possible words and the activation unit can then use the identified words to backtrack through the ASR grammar to activate the appropriate arcs. However, such an embodiment is not preferred, since the activation unit would have to search through the ASR grammar to identify and then activate the relevant arcs.  
      In the above embodiment, the key-presses entered by the user on the keyboard were used to confine the recognition vocabulary of the automatic speech recognition unit. As those skilled in the art will appreciate, this is not essential. For example, the keyboard processor may operate independently of the ASR unit and the controller may be arranged to display words from both the keyboard processor and the ASR unit. In such an embodiment, the controller may be arranged to give precedence to either the ASR candidate word or to the text input by the keyboard processor. This precedence may also depend on the number of key-presses that the user has made. For example, when only one or two key-presses have been made, the controller may place more emphasis on the ASR candidate word, whereas when three or four key-presses have been made the controller may place more emphasis on the predicted word generated by the keyboard processor.  
      In the above embodiment, the activation unit received data that identified words within a word dictionary corresponding to the input key-presses. The activation unit then retrieved arc data for those words which it used to activate the corresponding portions of the ASR grammar. In an alternative embodiment, the activation unit may simply receive a list of the key-presses that the user has entered. In such an embodiment, the word dictionary could include the sequences of key-presses together with the corresponding arcs within the ASR grammar. The activation unit would then use the received list of key-presses to look-up the appropriate arc data from the word dictionary, which it would then use to activate the corresponding portions of the ASR grammar.  
      In the above embodiment, a cellular telephone has been described which allows users to enter text using Roman letters (i.e. the characters used in written English). As those skilled in the art will appreciate the present invention can be applied to cellular telephones which allow the inputting of the symbols used in any language such as, for example, Arabic or Japanese symbols.  
      In the above embodiment, the automatic speech recognition unit was arranged to recognise words and to output recognised words to the control unit. In an alternative embodiment, the automatic speech recognition unit may be arranged to output a sequence (or lattice) of phonemes or other sub-word units as a recognition result. In such an embodiment, for any given input key sequence, the keyboard processor would output the different possible sequences of symbols to the control unit. The control unit can then convert each sequence of symbols into a corresponding sequence (or lattice) of phonemes (or other sub-word units) which it can then compare with the sequence (or lattice) of phonemes (or sub-word units) output by the automatic speech recognition unit. The control unit can then use the results of this comparison to identify the most likely sequence of symbols corresponding to the ambiguous input key sequence. The control unit can then display the appropriate stem or word corresponding to the most likely sequence.  
      A cellular telephone device was described which included a text editor for generating text messages in response to key-presses on an ambiguous keyboard and in response to speech recognised by a speech recogniser. The text editor and the speech recogniser may be formed from dedicated hardware circuits. Alternatively, the text editor and the automatic speech recognition circuit may be formed by a programmable processor which operates in accordance with stored software instructions which cause the processor to operate as the text editor and the speech recognition circuit. The software may be pre-stored in a memory of the cellular telephone or it may be downloaded on an appropriate carrier signal from, for example, the telephone network.