Patent Publication Number: US-7720682-B2

Title: Method and apparatus utilizing voice input to resolve ambiguous manually entered text input

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
   This application is a continuation-in-part of the following application and claims the benefit thereof under 35 USC 120: U.S. application Ser. No. 11/143,409 filed Jun. 1, 2005. The foregoing application (1) claims the 35 USC 119 benefit of U.S. Provisional Application No. 60/576,732 filed Jun. 2, 2004 and (2) claims the 35 USC 119 benefit under 35 USC 119 of U.S. Provisional Application No. 60/651,302 filed Feb. 8, 2005 and (2) is a continuation-in-part of U.S. application Ser. No. 10/866,634 filed Jun. 10, 2004 (which claims the benefit of U.S. Provisional Application 60/504,240 filed Sep. 19, 2003 and is also a continuation-in-part of U.S. application Ser. No. 10/176,933 filed Jun. 20, 2002 which is a continuation-in-part of U.S. application Ser. No. 09/454,406, filed Dec. 3, 1999, now U.S. Pat. No. 6,646,573 which itself claims priority based upon U.S. Provisional Application No. 60/110,890 filed Dec. 4, 1998) and (2) is a continuation-in-part of U.S. application Ser. No. 11/043,506 filed Jan. 25, 2005 now U.S. Pat. No. 7,319,957 (which claims the benefit of U.S. Provisional Application No. 60/544,170 filed Feb. 11, 2004). The foregoing applications in their entirety are incorporated by reference. 

   BACKGROUND 
   1. Technical Field 
   The invention relates to user manual entry of text using a digital data processing device. More particularly, the invention relates to computer driven operations to supplement a user&#39;s inherently ambiguous, manual text entry with voice input to disambiguate between different possible interpretations of the user&#39;s text entry. 
   2. Description of Related Art 
   For many years, portable computers have been getting smaller and smaller. Tremendous growth in the wireless industry has produced reliable, convenient, and nearly commonplace mobile devices such as cell phones, personal digital assistants (PDAs), global positioning system (GPS) units, etc. To produce a truly usable portable computer, the principle size-limiting component has been the keyboard. 
   To input data on a portable computer without a standard keyboard, people have developed a number of solutions. One such approach has been to use keyboards with less keys (“reduced-key keyboard”). Some reduced keyboards have used a 3-by-4 array of keys, like the layout of a touch-tone telephone. Although beneficial from a size standpoint, reduced-key keyboards come with some problems. For instance, each key in the array of keys contains multiple characters. For example, the “2” key represents “a” and “b” and “c”. Accordingly, each user-entered sequence is inherently ambiguous because each keystroke can indicate one number or several different letters. 
   T9® text input technology is specifically aimed at providing word-level disambiguation for reduced keyboards such as telephone keypads. T9 Text Input technology is described in various U.S. Patent documents including U.S. Pat. No. 5,818,437. In the case of English and other alphabet-based words, a user employs T9 text input as follows: 
   When inputting a word, the user presses keys corresponding to the letters that make up that word, regardless of the fact that each key represents multiple letters. For example, to enter the letter “a,” the user enters the “2” key, regardless of the fact that the “2” key can also represent “b” and “c.” T9 text input technology resolves the intended word by determining all possible letter combinations indicated by the user&#39;s keystroke entries, and comparing these to a dictionary of known words to see which one(s) make sense. 
   Beyond the basic application, T9 Text Input has experienced a number of improvements. Moreover, T9 text input and similar products are also available on reduced keyboard devices for languages with ideographic rather than alphabetic characters, such as Chinese. Still, T9 text input might not always provide the perfect level of speed and ease of data entry required by every user. 
   As a completely different approach, some small devices employ a digitizing surface to receive users&#39; handwriting. This approach permits users to write naturally, albeit in a small area as permitted by the size of the portable computer. Based upon the user&#39;s contact with the digitizing surface, handwriting recognition algorithms analyze the geometric characteristics of the user&#39;s entry to determine each character or word. Unfortunately, current handwriting recognition solutions have problems. For one, handwriting is generally slower than typing. Also, handwriting recognition accuracy is difficult to achieve with sufficient reliability. In addition, in cases where handwriting recognition algorithms require users to observe predefined character stroke patterns and orders, some users find this cumbersome to perform or difficult to learn. 
   A completely different approach for inputting data using small devices without a full-sized keyboard has been to use a touch-sensitive panel on which some type of keyboard overlay has been printed, or a touch-sensitive screen with a keyboard overlay displayed. The user employs a finger or a stylus to interact with the panel or display screen in the area associated with the desired key or letter. With a small overall size of such keyboards, the individual keys can be quite small. This can make it difficult for the average user to type accurately and quickly. 
   A number of built-in and add-on products offer word prediction for touch-screen and overlay keyboards. After the user carefully taps on the first letters of a word, the prediction system displays a list of the most likely complete words that start with those letters. If there are too many choices, however, the user has to keep typing until the desired word appears or the user finishes the word. Text entry is slowed rather than accelerated, however, by the user having to switch visual focus between the touch-screen keyboard and the list of word completions after every letter. Consequently, some users can find the touch-screen and overlay keyboards to be somewhat cumbersome or error-prone. 
   In view of the foregoing problems, and despite significant technical development in the area, users can still encounter difficulty or error when manually entering text on portable computers because of the inherent limitations of reduced-key keypads, handwriting digitizers, and touch-screen/overlay keyboards. 
   SUMMARY OF THE INVENTION 
   From a text entry tool, a digital data processing device receives inherently ambiguous user input. Independent of any other user input, the device interprets the received user input against a vocabulary to yield candidates, such as words (of which the user input forms the entire word or part such as a root, stem, syllable, affix) or phrases having the user input as one word. The device displays the candidates and applies speech recognition to spoken user input. If the recognized speech comprises one of the candidates, that candidate is selected. If the recognized speech forms an extension of a candidate, the extended candidate is selected. If the recognized speech comprises other input, various other actions are taken. 

   
     BRIEF DESCRIPTION OF FIGURES 
       FIG. 1  is a block diagram showing some components of an exemplary system for using voice input to resolve ambiguous manually entered text input. 
       FIG. 2  is a block diagram showing an exemplary signal bearing media. 
       FIG. 3  is a block diagram showing a different, exemplary signal bearing medium. 
       FIG. 4  is a perspective view of exemplary logic circuitry. 
       FIG. 5  is a block diagram of an exemplary digital data processing apparatus. 
       FIG. 6  is a flowchart of a computer executed sequence for utilizing user voice input to resolve ambiguous manually entered text input. 
       FIGS. 7-11  illustrate various examples of receiving and processing user input. 
       FIG. 12  is a flowchart of a computer executed sequence for using voice input to resolve ambiguous manually entered input of ideographic characters. 
   

   DETAILED DESCRIPTION 
   Introduction 
   One aspect of the disclosure concerns a handheld mobile device providing user operated text entry tool. This device may be embodied by various hardware components and interconnections, with one example being described by  FIG. 1 . The handheld mobile device of  FIG. 1  includes various processing subcomponents, each of which may be implemented by one or more hardware devices, software devices, a portion of one or more hardware or software devices, or a combination of the foregoing. The makeup of these subcomponents is described in greater detail below, with reference to an exemplary digital data processing apparatus, logic circuit, and signal bearing media. 
   Overall Structure 
     FIG. 1  illustrates an exemplary system  100  for using voice input to resolve ambiguous manually entered text input. The system  100  may be implemented as a PDA, cell phone, AM/FM radio, MP3 player, GPS, automotive computer, or virtually any other device with a reduced size keyboard or other entry facility such that users&#39; text entry includes some inherent ambiguity. For the sake of completeness, the user is shown at  101 , although the user does not actually form part of the system  100 . The user  101  enters all or part of a word, phrase, sentence, or paragraph using the user interface  102 . Data entry is inherently non-exact, in that each user entry could possibly represent different letters, digits, symbols, etc. 
   User Interface 
   The user interface  102  is coupled to the processor  140 , and includes various components. At minimum. the interface  102  includes devices for user speech input, user manual input, and output to the user. To receive manual user input, the interface  102  may include one or more text entry tools. One example is a handwriting digitizer  102   a , such as a digitizing surface. A different option of text entry tool is a key input  102   b  such as a telephone keypad, set of user-configurable buttons, reduced-keyset keyboard, or reduced-size keyboard where each key represents multiple alphanumeric characters. Another example of text entry tool is a soft keyboard, namely, a computer generated keyboard coupled with a digitizer, with some examples including a soft keyboard, touch-screen keyboard, overlay keyboard, auto-correcting keyboard, etc. Further examples of the key input  102   b  include mouse, trackball, joystick, or other non-key devices for manual text entry, and in this sense, the component name “key input” is used without any intended limitation. The use of joysticks to manually enter text is described in the following reference, which is incorporated herein in its entirety by this reference thereto. U.S. application Ser. No. 10/775,663, filed on Feb. 9, 2004 in the name of Pim van Meurs and entitled “System and Method for Chinese Input Using a Joystick.” The key input  102   b  may include one or a combination of the foregoing components. 
   Inherently, the foregoing text entry tools include some ambiguity. For example, there is never perfect certainty of identifying characters entered with a handwriting input device. Similarly, alphanumeric characters entered with a reduced-key keyboard can be ambiguous, because there are typically three letters and one number associated with each most keys. Keyboards can be subject to ambiguity where characters are small or positioned close together and prone to user error. 
   To provide output to the user  101 , the interface  102  includes an audio output  102   d , such as one or more speakers. A different or additional option for user output is a display  102   e  such as an LCD screen, CRT, plasma display, or other device for presenting human readable alphanumerics, ideographic characters, and/or graphics. 
   Processor 
   The system  100  includes a processor  140 , coupled to the user interface  102  and digital data storage  150 . The processor  140  includes various engines and other processing entities, as described in greater detail below. The storage  150  contains various components of digital data, also described in greater detail below. Some of the processing entities (such as the engines  115 , described below) are described with the processor  140 , whereas others (such as the programs  152 ) are described with the storage  150 . This is but one example, however, as ordinarily skilled artisans may change the implementation of any given processing entity as being hard-coded into circuitry (as with the processor  140 ) or retrieved from storage and executed (as with the storage  150 ). 
   The illustrated components of the processor  140  and storage  150  are described as follows: 
   A digitizer  105  digitizes speech from the user  101  and comprises an analog-digital converter, for example. Optionally, the digitizer  105  may be integrated with the voice-in feature  102   c . The decoder  109  comprises a facility to apply an acoustic model (not shown) to convert digitized voice signals from  105 , and namely users&#39; utterances, into phonetic data. A phoneme recognition engine  134  functions to recognize phonemes in the voice input. The phoneme recognition engine may employ any techniques known in the field to provide, for example, a list of candidates and associated probability of matching for each input of phoneme. A recognition engine  111  analyzes the data from  109  based on the lexicon and/or language model in the linguistic databases  119 , such analysis optionally including frequency or recency of use, surrounding context in the text buffer  113 , etc. In one embodiment, the engine  111  produces one or more N-best hypothesis lists. 
   Another component of the system  100  is the digitizer  107 . The digitizer provides a digital output based upon the handwriting input  102   a . The stroke/character recognition engine  130  is a module to perform handwriting recognition upon block, cursive, shorthand, ideographic character, or other handwriting output by the digitizer  107 . The stroke/character recognition engine  130  may employ any techniques known in the field to provide a list of candidates and associated probability of matching for each input for stroke and character. 
   The processor  140  further includes various disambiguation engines  115 , including in this example, a word disambiguation engine  115   a , phrase disambiguation engine  115   b , context disambiguation engine  115   c , and multimodal disambiguation engine  115   d.    
   The disambiguation engines  115  determine possible interpretations of the manual and/or speech input based on the lexicon and/or language model in the linguistic databases  119  (described below), optimally including frequency or recency of use, and optionally based on the surrounding context in a text buffer  113 . As an example, the engine  115  adds the best interpretation to the text buffer  113  for display to the user  101  via the display  102   e . All of the interpretations may be stored in the text buffer  113  for later selection and correction, and may be presented to the user  101  for confirmation via the display  102   e.    
   The multimodal disambiguation engine  115   d  compares ambiguous input sequence and/or interpretations against the best or N-best interpretations of the speech recognition from recognition engine  111  and presents revised interpretations to the user  101  for interactive confirmation via the interface  102 . In an alternate embodiment, the recognition engine  111  is incorporated into the disambiguation engine  115 , and mutual disambiguation occurs as an inherent part of processing the input from each modality in order to provide more varied or efficient algorithms. In a different embodiment, the functions of engines  115  may be incorporated into the recognition engine  111 ; here, ambiguous input and the vectors or phoneme tags are directed to the speech recognition system for a combined hypothesis search. 
   In another embodiment, the recognition engine  111  uses the ambiguous interpretations from multimodal disambiguation engine  115   d  to filter or excerpt a lexicon from the linguistic databases  119 , with which the recognition engine  111  produces one or more N-best lists. In another embodiment, the multimodal disambiguation engine  115   d  maps the characters (graphs) of the ambiguous interpretations and/or words in the N-best list to vectors or phonemes for interpretation by the recognition engine  111 . 
   The recognition and disambiguation engines  111 ,  115  may update one or more of the linguistic databases  119  to add novel words or phrases that the user  101  has explicitly spelled or compounded, and to reflect the frequency or recency of use of words and phrases entered or corrected by the user  101 . This action by the engines  111 ,  115  may occur automatically or upon specific user direction. 
   In one embodiment, the engine  115  includes separate modules for different parts of the recognition and/or disambiguation process, which in this example include a word-based disambiguating engine  115   a , a phrase-based recognition or disambiguating engine  115   b , a context-based recognition or disambiguating engine  115   c , multimodal disambiguating engine  115   d , and others. In one example, some or all of the components  115   a - 115   d  for recognition and disambiguation are shared among different input modalities of speech recognition and reduced keypad input. 
   In one embodiment, the context based disambiguating engine  115   c  applies contextual aspects of the user&#39;s actions toward input disambiguation. For example, where there are multiple vocabularies  156  (described below), the engine  115   c  conditions selection of one of the vocabularies  156  upon selected user location, e.g. whether the user is at work or at home; the time of day, e.g. working hours vs. leisure time; message recipient; etc. 
   Storage 
   The storage  150  includes application programs  152 , a vocabulary  156 , linguistic database  119 , text buffer  113 , and an operating system  154 . Examples of application programs include word processors, messaging clients, foreign language translators, speech synthesis software, etc. 
   The text buffer  113  comprises the contents of one or more input fields of any or all applications being executed by the device  100 . The text buffer  113  includes characters already entered and any supporting information needed to re-edit the text, such as a record of the original manual or vocal inputs, or for contextual prediction or paragraph formatting. 
   The linguistic databases  119  include information such as lexicon, language model, and other linguistic information. Each vocabulary  156  includes or is able to generate a number of predetermined words, characters, phrases, or other linguistic formulations appropriate to the specific application of the device  100 . One specific example of the vocabulary  156  utilizes a word list  156   a , a phrase list  156   b , and a phonetic/tone table  156   c . Where appropriate, the system  100  may include vocabularies for different applications, such as different languages, different industries, e.g., medical, legal, part numbers, etc. A “word” is used to refer any linguistic object, such as a string of one or more characters or symbols forming a word, word stem, prefix or suffix, syllable, abbreviation, chat slang, emoticon, user ID or other identifier of data, URL, or ideographic character sequence. Analogously, “phrase” is used to refer to a sequence of words which may be separated by a space or some other delimiter depending on the conventions of the language or application. As discussed in greater detail below, words  156   a  may also include ideographic language characters, and in which cases phrases comprise phrases of formed by logical groups of such characters. Optionally, the vocabulary word and/or phrase lists may be stored in the database  119  or generated from the database  119 . 
   In one example, the word list  156   a  comprises a list of known words in a language for all modalities, so that there are no differences in vocabulary between input modalities. The word list  156   a  may further comprise usage frequencies for the corresponding words in the language. In one embodiment, a word not in the word list  156   a  for the language is considered to have a zero frequency. Alternatively, an unknown or newly added word may be assigned a very small frequency of usage. Using the assumed frequency of usage for the unknown words, known and unknown words can be processed in a substantially similar fashion. Recency of use may also be a factor in computing and comparing frequencies. The word list  156   a  can be used with the word based recognition or disambiguating engine  115   a  to rank, eliminate, and/or select word candidates determined based on the result of the pattern recognition engine, e.g. the stroke/character recognition engine  130  or the phoneme recognition engine  134 , and to predict words for word completion based on a portion of user inputs. 
   Similarly, the phrase list  156   b  may comprise a list of phrases that includes two or more words, and the usage frequency information, which can be used by the phrase-based recognition or disambiguation engine  115   b  and can be used to predict words for phrase completion. 
   The phonetic/tone table  156   c  comprises a table, linked list, database, or any other data structure that lists various items of phonetic information cross-referenced against ideographic items. The ideographic items include ideographic characters, ideographic radicals, logographic characters, lexigraphic symbols, and the like, which may be listed for example in the word list  156   a . Each item of phonetic information includes pronunciation of the associated ideographic item and/or pronunciation of one or more tones, etc. The table  156   c  is optional, and may be omitted from the vocabulary  156  if the system  100  is limited to English language or other non-ideographic applications. 
   In one embodiment, the processor  140  automatically updates the vocabulary  156 . In one example, the selection module  132  may update the vocabulary during operations of making/requesting updates to track recency of use or to add the exact-tap word when selected, as mentioned in greater detail below. In a more general example, during installation, or continuously upon the receipt of text messages or other data, or at another time, the processor  140  scans information files (not shown) for words to be added to its vocabulary. Methods for scanning such information files are known in the art. In this example, the operating system  154  or each application  152  invokes the text-scanning feature. As new words are found during scanning, they are added to a vocabulary module as low frequency words and, as such, are placed at the end of the word lists with which the words are associated. Depending on the number of times that a given new word is detected during a scan, it is assigned a higher priority, by promoting it within its associated list, thus increasing the likelihood of the word appearing in the word selection list during information entry. Depending on the context, such as an XML tag on the message or surrounding text, the system may determine the appropriate language to associate the new word with. Standard pronunciation rules for the current or determined language may be applied to novel words in order to arrive at their phonetic form for future recognition. Optionally, the processor  140  responds to user configuration input to cause the additional vocabulary words to appear first or last in the list of possible words, e.g. with special coloration or highlighting, or the system may automatically change the scoring or order of the words based on which vocabulary module supplied the immediately preceding accepted or corrected word or words. 
   In one embodiment, the vocabulary  156  also contains substitute words for common misspellings and key entry errors. The vocabulary  156  may be configured at manufacture of the device  100 , installation, initial configuration, reconfiguration, or another occasion. Furthermore, the vocabulary  156  may self-update when it detects updated information via web connection, download, attachment of an expansion card, user input, or other event. 
   Exemplary Digital Data Processing Apparatus 
   As mentioned above, data processing entities described in this disclosure may be implemented in various forms. One example is a digital data processing apparatus, as exemplified by the hardware components and interconnections of the digital data processing apparatus  500  of  FIG. 5 . 
   The apparatus  500  includes a processor  502 , such as a microprocessor, personal computer, workstation, controller, microcontroller, state machine, or other processing machine, coupled to digital data storage  504 . In the present example, the storage  504  includes a fast-access storage  506 , as well as nonvolatile storage  508 . The fast-access storage  506  may comprise random access memory (“RAM”), and may be used to store the programming instructions executed by the processor  502 . The nonvolatile storage  508  may comprise, for example, battery backup RAM, EEPROM, flash PROM, one or more magnetic data storage disks such as a hard drive, a tape drive, or any other suitable storage device. The apparatus  500  also includes an input/output  510 , such as a line, bus, cable, electromagnetic link, or other means for the processor  502  to exchange data with other hardware external to the apparatus  500 . 
   Despite the specific foregoing description, ordinarily skilled artisans (having the benefit of this disclosure) will recognize that the apparatus discussed above may be implemented in a machine of different construction, without departing from the scope of the invention. As a specific example, one of the components  506 ,  508  may be eliminated; furthermore, the storage  504 ,  506 , and/or  508  may be provided on-board the processor  502 , or even provided externally to the apparatus  500 . 
   Signal-Bearing Media 
   In contrast to the digital data processing apparatus described above, a different aspect of this disclosure concerns one or more signal-bearing media tangibly embodying a program of machine-readable instructions executable by such a digital processing apparatus. In one example, the machine-readable instructions are executable to carry out various functions related to this disclosure, such as the operations described in greater detail below. In another example, the instructions upon execution serve to install a software program upon a computer, where such software program is independently executable to perform other functions related to this disclosure, such as the operations described below. 
   In any case, the signal-bearing media may take various forms. In the context of  FIG. 5 , such a signal-bearing media may comprise, for example, the storage  504  or another signal-bearing media, such as an optical storage disc  300  ( FIG. 3 ), directly or indirectly accessible by a processor  502 . Whether contained in the storage  506 , disc  300 , or elsewhere, the instructions may be stored on a variety of machine-readable data storage media. Some examples include direct access storage, e.g. a conventional hard drive, redundant array of inexpensive disks (“RAID”), or another direct access storage device (“DASD”); serial-access storage such as magnetic or optical tape, electronic non-volatile memory e.g. ROM, EPROM, flash PROM, or EEPROM; battery backup RAM, optical storage e.g. CD-ROM, WORM, DVD, digital optical tape; or other suitable signal-bearing media. In one embodiment, the machine-readable instructions may comprise software object code, compiled from a language such as assembly language, C, etc. 
   Logic Circuitry 
   In contrast to the signal-bearing media and digital data processing apparatus discussed above, a different embodiment of this disclosure uses logic circuitry instead of computer-executed instructions to implement processing entities of the disclosure. Depending upon the particular requirements of the application in the areas of speed, expense, tooling costs, and the like, this logic may be implemented by constructing an application-specific integrated circuit (ASIC) having thousands of tiny integrated transistors.  FIG. 4  shows one example in the form of the circuit  400 . Such an ASIC may be implemented with CMOS, TTL, VLSI, or another suitable construction. Other alternatives include a digital signal processing chip (DSP), discrete circuitry (such as resistors, capacitors, diodes, inductors, and transistors), field programmable gate array (FPGA), programmable logic array (PLA), programmable logic device (PLD), and the like. 
   Operation 
   Having described the structural features of the present disclosure, the operational aspect of the disclosure will now be described. As mentioned above, the operational aspect of the disclosure generally involves various techniques to resolve intentionally ambiguous user input entered upon a text entry tool of a handheld mobile device. 
   Operational Sequence 
     FIG. 6  shows a sequence  600  to illustrate one example of the method aspect of this disclosure. In one application, this sequence serves to resolve inherently ambiguous user input entered upon a text entry tool of a handheld digital data processing device. For ease of explanation, but without any intended limitation, the example of  FIG. 6  is described in the context of the device of  FIG. 1 , as described above. 
   In step  602 , the text entry tool e.g. device  102   a  and/or  102   b , of the user interface  102  receives user input representing multiple possible character combinations. Depending upon the structure of the device, some examples of step  602  include receiving user entry via a telephone keypad where each key corresponds to multiple alphanumeric characters, or receiving input via handwriting digitizer, or receiving input via computer display and co-located digitizing surface, etc. 
   In step  604 , independent of any other user input, the device interprets the received user input against the vocabulary  156  and/or linguistic databases  119  to yield a number of word candidates, which may also be referred to as “input sequence interpretations” or “selection list choices.” As a more particular example, the word list  156   a  may be used. 
   In one embodiment, one of the engines  130 ,  115   a ,  115   b  processes the user input (step  604 ) to determine possible interpretations for the user entry so far. Each word candidate comprises one of the following: 
   (1) a word of which the user input forms a stem, root, syllable, or affix; 
   (2) a phrase of which the user input forms one or more words or parts of words; (3) a complete word represented by the user input. 
   Thus, the term “word” in “word candidate” is used for the sake of convenient explanation without being necessarily limited to “words” in a technical sense. In some embodiments, user inputs (step  602 ) for only “root” words are needed, such as for highly agglutinative languages and those with verb-centric phrase structures that append or prepend objects and subjects and other particles. Additionally, the interpretation  604  may be conducted such that (1) each candidate begins with letters corresponding to the user input, (2) each candidate includes letters corresponding to the user input, the letters occurring between starting and ending letters of the candidate, etc. 
   In various embodiments, such as when manual key-in  102   b  is an auto-correcting keyboard displayed on a touch-screen device, the interpretation  604  includes a character sequence (the unambiguous interpretation or “exact-tap” sequence) containing each character that is the best interpretation of the user&#39;s input, such as the closest character to each stylus tap, which the user may choose (in step  614 ) if the desired word is not already in the linguistic databases  119 . In some embodiments, such as when the manual key-in  102   b  is a reduced keyboard such as a standard phone keypad, the unambiguous interpretation is a two-key or multi-tap interpretation of the key sequence. In some embodiments, after the user selects such the unambiguous interpretation (step  614 , below), the device automatically or upon user request or confirmation adds the unambiguous interpretation to the vocabulary under direction of the selection module  132 . 
   In one example, the interpretation step  604  places diacritics such as vowel accents upon the proper characters of each word without the user indicating that a diacritic mark is needed. 
   In step  606 , one or more of the engines  115 ,  130 ,  115   a ,  115   b  rank the candidate words according to likelihood of representing the user&#39;s intent. The ranking operation  606  may use criteria such as: whether the candidate word is present in the vocabulary  156 ; frequency of use of the candidate word in general use; frequency of use of the candidate word by the user; etc. Usage frequencies and other such data for the ranking operation  606  may be obtained from the vocabulary modules  156  and/or linguistic databases  119 . Step  606  is optional, and may be omitted to conserve processing effort, time, memory, etc. 
   In step  608 , the processor  140  visibly presents the candidates at the interface  102  for viewing by the user. In embodiments where the candidates are ranked (pursuant to step  606 ), the presentation of step  608  may observe this ordering. Optionally, step  608  may display the top-ranked candidate so as to focus attention upon it, for example, by inserting the candidate at a displayed cursor location, or using another technique such as bold, highlighting, underline, etc. 
   In step  610 , the processor  140  uses the display  102   e  or audio-out  102   d  to solicit the user to speak an input. Also in step  610 , the processor  140  receives the user&#39;s spoken input via voice input device  102   c  and front-end digitizer  105 . In one example, step  610  comprises an audible prompt e.g. synthesized voice saying “choose word”; visual message e.g. displaying “say phrase to select it”, iconic message e.g. change in cursor appearance or turning a LED on; graphic message e.g. change in display theme, colors, or such; or another suitable prompt. In one embodiment, step  610 &#39;s solicitation of user input may be skipped, in which case such prompt is implied. 
   In one embodiment, the device  100  solicits or permits a limited set of speech utterances representing a small number of unique inputs; as few as the number of keys on a reduced keypad, or as many as the number of unique letter forms in a script or the number of consonants and vowels in a spoken language. The small distinct utterances are selected for low confusability, resulting in high recognition accuracy, and are converted to text using word-based and/or phrase-based disambiguation engines. This capability is particularly useful in a noisy or non-private environment, and vital to a person with a temporary or permanent disability that limits use of the voice. Recognized utterances may include mouth clicks and other non-verbal sounds. 
   In step  612 , the linguistic pattern recognition engine  111  applies speech recognition to the data representing the user&#39;s spoken input from step  610 . In one example, speech recognition  612  uses the vocabulary of words and/or phrases in  156   a ,  156   b . In another example, speech recognition  612  utilizes a limited vocabulary, such as the most likely interpretations matching the initial manual input (from  602 ), or the candidates displayed in step  608 . Alternately, the possible words and/or phrases, or just the most likely interpretations, matching the initial manual input serve as the lexicon for the speech recognition step. This helps eliminate incorrect and irrelevant interpretations of the spoken input. 
   In one embodiment, step  612  is performed by a component such as the decoder  109  converting an acoustic input signal into a digital sequence of vectors that are matched to potential phones given their context. The decoder  109  matches the phonetic forms against a lexicon and language model to create an N-best list of words and/or phrases for each utterance. The multimodal disambiguation engine  115   d  filters these against the manual inputs so that only words that appear in both lists are retained. 
   Thus, because the letters mapped to each telephone key (such as “A B C” on the “2” key) are typically not acoustically similar, the system can efficiently rule out the possibility that an otherwise ambiguous sound such as the plosive /b/ or /p/ constitutes a “p”, since the user pressed the “2” key (containing “A B C”) rather than the “7” key (containing “P Q R S”). Similarly, the system can rule out the “p” when the ambiguous character being resolved came from tapping the auto-correcting QWERTY keyboard in the “V B N” neighborhood rather than in the “I O P” neighborhood. Similarly, the system can rule out the “p” when an ambiguous handwriting character is closer to a “B” or “3” than a “P” or “R.” 
   Optionally, if the user inputs more than one partial or complete word in a series, delimited by a language-appropriate input like a space, the linguistic pattern recognition engine  111  or multimodal disambiguation engine  115   d  uses that information as a guide to segment the user&#39;s continuous speech and looks for boundaries between words. For example, if the interpretations of surrounding phonemes strongly match two partial inputs delimited with a space, the system determines the best place to split a continuous utterance into two separate words. In another embodiment, “soundex” rules refine or override the manual input interpretation in order to better match the highest-scoring speech recognition interpretations, such as to resolve an occurrence of the user accidentally adding or dropping a character from the manual input sequence. 
   Step  614  is performed by a component such as the multimodal disambiguation engine  115   d , selection module  132 , etc. Step  614  performs one or more of the following actions. In one embodiment, responsive to the recognized speech forming an utterance matching one of the candidates, the device selects the candidate. In other words, if the user speaks one of the displayed candidates to select it. In another embodiment, responsive to the recognized speech forming an extension of a candidate, the device selects the extended candidate. As an example of this, the user speaks “nationality” when the displayed candidate list includes “national,” causing the device to select “nationality.” In another embodiment, responsive to the recognized speech forming a command to expand one of the candidates, the multimodal disambiguation engine  115   d  or one of components  115 ,  132  retrieves from the vocabulary  156  or linguistic databases  119  one or more words or phrases that include the candidate as a subpart and visibly presents them for the user to select from. Expansion may include words with the candidate as a prefix, suffix, root, syllable, or other subcomponent. 
   Optionally, the phoneme recognition engine  134  and linguistic pattern recognition engine  111  may employ known speech recognition features to improve recognition accuracy by comparing the subsequent word or phrase interpretations actually selected against the original phonetic data. 
   Operational Examples 
     FIGS. 7-11  illustrate various exemplary scenarios in furtherance of  FIG. 6 .  FIG. 7  illustrates contents of a display  701  (serving as an example of  102   e ) to illustrate the use of handwriting to enter characters and the use of voice to complete the entry. First, in step  602  the device receives the following user input: the characters “t e c”, handwritten in the digitizer  700 . The device  100  interprets ( 604 ) and ranks ( 606 ) the characters, and provides a visual output  702 / 704  of the ranked candidates. Due to limitations of screen size, not all of the candidates are presented in the list  702 / 704 . 
   Even though “tec” is not a word in the vocabulary, the device includes it as one of the candidate words  704  (step  604 ). Namely, “tec” is shown as the “exact-tap” word choice i.e. best interpretation of each individual letter. The device  100  automatically presents the top-ranked candidate ( 702 ) in a manner to distinguish it from the others. In this example, the top-ranked candidate “the” is presented first in the list  700 . 
   In step  610 , the user speaks /tek/ in order to select the word as entered in step  602 , rather than the system-proposed word “the.” Alternatively, the user may utter “second” (since “tec” is second in the list  704 ) or another input to select “tec” from the list  704 . The device  100  accepts the word as the user&#39;s choice (step  614 ), and enters “t-e-c” at the cursor as shown in  FIG. 8 . As part of step  614 , the device removes presentation of the candidate list  704 . 
   In a different embodiment, referring to  FIG. 7 , the user had entered “t”, “e”, “c” (step  602 ) but merely in the process of entering the full word “technology.” In this embodiment, the device provides a visual output  702 / 704  of the ranked candidates, and automatically enters the top-ranked candidate (at  702 ) adjacent to a cursor as in  FIG. 7 . In contrast to  FIG. 8 , however, the user then utters ( 610 )/teknolōjē/ in order to select this as an expansion of “tec.” Although not visibly shown in the list  702 / 704 , the word “technology” is nonetheless included in the list of candidates, and may be reached by the user scrolling through the list. Here, the user skips scrolling, utters /teknolōjē/ at which point the device accepts “technology” as the user&#39;s choice (step  614 ), and enters “technology” at the cursor as shown in  FIG. 9 . As part of step  614 , the device removes presentation of the candidate list  704 . 
     FIG. 10  describes a different example to illustrate the use of an on-screen keyboard to enter characters and the use of voice to complete the entry. The on-screen keyboard, for example, may be implemented as taught by U.S. Pat. No. 6,081,190. In the example of  FIG. 10 , the user taps the sequence of letters “t”, “e”, “c” by stylus (step  602 ). In response, the device presents (step  608 ) the word choice list  1002 , namely “rev, tec, technology, received, recent, record.” Responsive to user utterance ( 610 ) of a word in the list  1002  such as “technology” (visible in the list  1002 ) or “technical” (present in the list  1002  but not visible), the device accepts such as the user&#39;s intention (step  614 ) and enters the word at the cursor  1004 . 
     FIG. 11  describes a different example to illustrate the use of a keyboard of reduced keys (where each key corresponds to multiple alphanumeric characters) to enter characters, and the use of voice to complete the entry. In this example, the user enters (step  602 ) hard keys 8 3 2, indicating the sequence of letters “t”, “e”, “c.” In response, the device presents (step  608 ) the word choice list  1102 . Responsive to user utterance ( 610 ) of a word in the list  1102  such as “technology” (visible in the list  1102 ) or “teachers” (present in the list  1102  but not visible), the device accepts such as the user&#39;s intention (step  614 ) and enters the selected word at the cursor  1104 . 
   Example for Ideographic Languages 
   Broadly, many aspects of this disclosure are applicable to text entry systems for languages written with ideographic characters on devices with a reduced keyboard or handwriting recognizer. For example, pressing the standard phone key “7” (where the Pinyin letters “P Q R S” are mapped to the “7” key) begins entry of the syllables “qing” or “ping”; after speaking the desired syllable /tsing/, the system is able to immediately determine that the first grapheme is in fact a “q” rather than a “p”. Similarly, with a stroke-order input system, after the user presses one or more keys representing the first stroke categories for the desired character, the speech recognition engine can match against the pronunciation of only the Chinese characters beginning with such stroke categories, and is able to offer a better interpretation of both inputs. Similarly, beginning to draw one or more characters using a handwritten ideographic character recognition engine can guide or filter the speech interpretation or reduce the lexicon being analyzed. 
   Though an ambiguous stroke-order entry system or a handwriting recognition engine may not be able to determine definitively which handwritten stroke was intended, the combination of the stroke interpretation and the acoustic interpretation sufficiently disambiguates the two modalities of input to offer the user the intended character. In one embodiment of this disclosure, the speech recognition step is used to select the character, word, or phrase from those displayed based on an input sequence in a conventional stroke-order entry or handwriting system for ideographic languages. In another embodiment, the speech recognition step is used to add tonal information for further disambiguation in a phonetic input system. The implementation details related to ideographic languages are discussed in greater detail as follows. 
     FIG. 12  shows a sequence  1200  to illustrate another example of the method aspect of this disclosure. This sequence serves to resolve inherently ambiguous user input in order to aid in user entry of words and phrases comprised of ideographic characters. Although the term “ideographic” is used in these examples, the operations  1200  may be implemented with many different logographic, ideographic, lexigraphic, morpho-syllabic, or other such writing systems that use characters to represent individual words, concepts, syllables, morphemes, etc. The notion of ideographic characters herein is used without limitation, and shall include the Chinese pictograms, Chinese ideograms proper, Chinese indicatives, Chinese sound-shape compounds (phonologograms), Japanese characters (Kanji), Korean characters (Hanja), and other such systems. Furthermore, the system  100  may be implemented to a particular standard, such as traditional Chinese characters, simplified Chinese characters, or another standard. For ease of explanation, but without any intended limitation, the example of  FIG. 12  is described in the context of  FIG. 1 , as described above. 
   In step  1202 , one of the input devices  102   a / 102   b  receives user input used to identify one or more intended ideographic characters or subcomponents. The user input may specify handwritten strokes, categories of handwritten strokes, phonetic spelling, tonal input; etc. Depending upon the structure of the device  100 , this action may be carried out in different ways. One example involves receiving user entry via a telephone keypad ( 102   b ) where each key corresponds to a stroke category. For example, a particular key may represent all downward-sloping strokes. Another example involves receiving user entry via handwriting digitizer ( 102   a ) or a directional input device of  102  such as a joystick where each gesture is mapped to a stroke category. In one example, step  1202  involves the interface  102  receiving the user making handwritten stroke entries to enter the desired one or more ideographic characters. As still another option, step  1202  may be carried out by an auto-correcting keyboard system ( 102   b ) for a touch-sensitive surface or an array of small mechanical keys, where the user enters approximately some or all of the phonetic spelling, components, or strokes of one or more ideographic characters. 
   Various options for receiving input in step  1202  are described by the following reference documents, each incorporated herein by reference. U.S. application Ser. No. 10/631,543, filed on Jul. 30, 2003 and entitled “System and Method for Disambiguating Phonetic Input.” U.S. application Ser. No. 10/803,255 filed on Mar. 17, 2004 and entitled “Phonetic and Stroke Input Methods of Chinese Characters and Phrases.” U.S. Application No. 60/675,059 filed Apr. 25, 2005 and entitled “Word and Phrase Prediction System for Handwriting.” U.S. application Ser. No. 10/775,483 filed Feb. 9, 2004 and entitled “Keyboard System with Automatic Correction.” U.S. application Ser. No. 10/775,663 filed Feb. 9, 2004 and entitled “System and Method for Chinese Input Using a Joystick.” 
   Also in step  1202 , independent of any other user input, the device interprets the received user input against a first vocabulary to yield a number of candidates each comprising at least one ideographic character. More particularly, the device interprets the received strokes, stroke categories, spellings, tones, or other manual user input against the character listing from the vocabulary  156  (e.g.,  156   a ), and identifies resultant candidates in the vocabulary that are consistent with the user&#39;s manual input. Step  1202  may optionally perform pattern recognition and/or stroke filtering, e.g. on handwritten input, to identify those candidate characters that could represent the user&#39;s input thus far. 
   In step  1204 , which is optional, the disambiguation engines  115  order the identified candidate characters (from  1202 ) based on the likelihood that they represent what the user intended by his/her entry. This ranking may be based on information such as: (1) general frequency of use of each character in various written or oral forms, (2) the user&#39;s own frequency or recency of use, (3) the context created by the preceding and/or following characters, (4) other factors. The frequency information may be implicitly or explicitly stored in the linguistic databases  119  or may be calculated as needed. 
   In step  1206 , the processor  140  causes the display  102   e  to visibly present some or all of the candidates (from  1202  or  1204 ) depending on the size and other constraints of the available display space. Optionally, the device  100  may present the candidates in the form of a scrolling list. 
   In one embodiment, the display action of step  1206  is repeated after each new user input, to continually update (and in most cases narrow) the presented set of candidates ( 1204 ,  1206 ) and permit the user to either select a candidate character or continue the input ( 1202 ). In another embodiment, the system allows input ( 1202 ) for an entire word or phrase before displaying any of the constituent characters are displayed ( 1206 ). 
   In one embodiment, the steps  1202 ,  1204 ,  1206  may accommodate both single and multi-character candidates. Here, if the current input sequence represents more than one character in a word or phrase, then the steps  1202 ,  1204 , and  1206  identify, rank, and display multi-character candidates rather than single character candidates. To implement this embodiment, step  1202  may recognize prescribed delimiters as a signal to the system that the user has stopped his/her input, e.g. strokes, etc., for the preceding character and will begin to enter them for the next character. Such delimiters may be expressly entered (such as a space or other prescribed key) or implied from the circumstances of user entry (such as by entering different characters in different displayed boxes or screen areas). 
   Without invoking the speech recognition function (described below), the user may proceed to operate the interface  102  (step  1212 ) to accept one of the selections presented in step  1206 . Alternatively, if the user does not make any selection ( 1212 ), then step  1206  may automatically proceed to step  1208  to receive speech input. As still another option, the interface  102  in step  1206  may automatically prompt the user to speak with an audible prompt, visual message, iconic message, graphic message, or other prompt. Upon user utterance, the sequence  1200  passes from  1206  to  1208 . As still another alternative, the interface  102  may require (step  1206 ) the user to press a “talk” button or take other action to enable the microphone and invoke the speech recognition step  1208 . In another embodiment, the manual and vocal inputs are nearly simultaneous or overlapping. In effect, the user is voicing what he or she is typing. 
   In step  1208 , the system receives the user&#39;s spoken input via front-end digitizer  105  and the linguistic pattern recognition engine  111  applies speech recognition to the data representing the user&#39;s spoken input. In one embodiment, the linguistic pattern recognition engine  111  matches phonetic forms against a lexicon of syllables and words (stored in linguistic databases  119 ) to create an N-best list of syllables, words, and/or phrases for each utterance. In turn, the disambiguation engines  115  use the N-best list to match the phonetic spellings of the single or multi-character candidates from the stroke input, so that only the candidates whose phonetic forms also appear in the N-best list are retained (or become highest ranked in step  1210 ). In another embodiment, the system uses the manually entered phonetic spelling as a lexicon and language model to recognize the spoken input. 
   In one embodiment, some or all of the inputs from the manual input modality represent only the first letter of each syllable or only the consonants of each word. The system recognizes and scores the speech input using the syllable or consonant markers, filling in the proper accompanying letters or vowels for the word or phrase. For entry of Japanese text, for example, each keypad key is mapped to a consonant row in a 50 sounds table and the speech recognition helps determine the proper vowel or “column” for each syllable. In another embodiment, some or all of the inputs from the manual input modality are unambiguous. This may reduce or remove the need for the word disambiguation engine  115   a  in  FIG. 1 , but still requires the multimodal disambiguation engine  115   d  to match the speech input, in order to prioritize the desired completed word or phrase above all other possible completions or to identify intervening vowels. 
   Further, in some languages, such as Indic languages, the vocabulary module may employ templates of valid sub-word sequences to determine which word component candidates are possible or likely given the preceding inputs and the word candidates being considered. In other languages, pronunciation rules based on gender help further disambiguate and recognize the desired textual form. 
   Step  1208  may be performed in different ways. In one option, when the recognized speech forms an utterance including pronunciation of one of the candidates from  1206 , the processor  102  selects that candidate. In another option, when the recognized speech forms an utterance including pronunciation of phonetic forms of any candidates, the processor updates the display (from  1206 ) to omit characters other than those candidates. In still another option, when the recognized speech is an utterance potentially pronouncing any of a subset of the candidates, the processor updates the display to omit others than the candidates of the subset. In another option, when the recognized speech is an utterance including one or more tonal features corresponding to one or more of the candidates, the processor  102  updates the display (from  1206 ) to omit characters other than those candidates. 
   After step  1208 , step  1210  ranks the remaining candidates according to factors such as the speech input. For example, the linguistic pattern recognition engine  111  may provide probability information to the multimodal disambiguation engine  115   d  so that the most likely interpretation of the stroke or other user input and of the speech input is combined with the frequency information of each character, word, or phrase to offer the most likely candidates to the user for selection. As additional examples, the ranking ( 1210 ) may include different or additional factors such as: the general frequency of use of each character in various written or oral forms; the user&#39;s own frequency or recency of use; the context created by the preceding and/or following characters; etc. 
   After step  1210 , step  1206  repeats in order to display the character/phrase candidates prepared in step  1210 . Then, in step  1212 , the device accepts the user&#39;s selection of a single-character or multi-character candidate, indicated by some input means  102   a / 102   c / 102   b  such as tapping the desired candidate with a stylus. The system may prompt the user to make a selection, or to input additional strokes or speech, through visible, audible, or other means as described above. 
   In one embodiment, the top-ranked candidate is automatically selected when the user begins a manual input sequence for the next character. In another embodiment, if the multimodal disambiguation engine  115   d  identifies and ranks one candidate above the others in step  1210 , the system  100  may proceed to automatically select that candidate in step  1212  without waiting for further user input. In one embodiment, the selected ideographic character or characters are added at the insertion point of a text entry field in the current application and the input sequence is cleared. The displayed list of candidates may then be populated with the most likely characters to follow the just-selected character(s). 
   Other Embodiments 
   While the foregoing disclosure shows a number of illustrative embodiments, it will be apparent to those skilled in the art that various changes and modifications can be made herein without departing from the scope of the invention as defined by the appended claims. Furthermore, although elements of the invention may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated. Additionally, ordinarily skilled artisans will recognize that operational sequences must be set forth in some specific order for the purpose of explanation and claiming, but the present invention contemplates various changes beyond such specific order. 
   In addition, those of ordinary skill in the relevant art will understand that information and signals may be represented using a variety of different technologies and techniques. For example, any data, instructions, commands, information, signals, bits, symbols, and chips referenced herein may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, other items, or a combination of the foregoing. 
   Moreover, ordinarily skilled artisans will appreciate that any illustrative logical blocks, modules, circuits, and process steps described herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the invention. 
   The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. 
   The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a wireless communications device. In the alternative, the processor and the storage medium may reside as discrete components in a wireless communications device. 
   The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.