Patent Publication Number: US-11651765-B2

Title: Recognizing accented speech

Description:
RELATED APPLICATIONS 
     This patent application is a continuation of, and claims priority under 35 U.S.C. § 120 from, U.S. patent application Ser. No. 16/417,867, filed on May 21, 2019, which is a continuation of U.S. patent application Ser. No. 15/465,345, filed on Mar. 21, 2017, which is a continuation of U.S. patent application Ser. No. 15/464,668, filed on Mar. 21, 2017, which is a continuation of U.S. patent application Ser. No. 13/772,373, filed on Feb. 21, 2013. The disclosures of these prior applications are considered part of the disclosure of this application and are hereby incorporated by reference in their entireties 
    
    
     BACKGROUND 
     Current speech-recognition technologies are quite poor at recognizing speech when spoken with an accent. To address this problem, one partial solution tracks corrections made by a user in response to a current technology&#39;s failure to correctly recognize a word. This partial solution can be frustrating to users with accents, as they often have to correct many incorrectly recognized words before these current technologies improve their recognition, often so many times that a user gives up on voice recognition entirely. Even for those users that take the time and endure the frustration, many current technologies still inadequately recognize a user&#39;s speech when that user has an accent. 
     Another partial solution to address this problem requires a user to go to a special user interface and speak a list of particular words. Requiring users with accents to find this special user interface and speak a list of words does not provide an excellent user experience, and thus often will simply not be performed by users. Further, requiring this effort from users does not enable current technologies to recognize accents sufficiently well. Further still, even if a user that owns a device goes to this effort, it is unlikely to be performed by another user borrowing the owner&#39;s device, such as when a device&#39;s owner is driving and a passenger uses the owner&#39;s device. 
    
    
     
       BRIEF DESCRIPTION OF THE DR A WINGS 
       Techniques and apparatuses for recognizing accented speech are described with reference to the following drawings. The same numbers are used throughout the drawings to reference like features and components. 
         FIG.  1    illustrates an example environment in which techniques for recognizing accented speech can be implemented. 
         FIG.  2    illustrates example linguistic and accent libraries of  FIG.  1   . 
         FIG.  3    illustrates example methods for recognizing accented speech using an accent library determined based on device data. 
         FIG.  4    illustrates example methods for altering an accent library to more-accurately recognize accented speech. 
         FIG.  5    illustrates example methods for recognizing speech at a speech recognition level based on an application field, which may use an accent library. 
         FIG.  6    illustrates an example application having application fields. 
         FIG.  7    illustrates various components of an example apparatus that can implement techniques for recognizing accented speech. 
     
    
    
     DETAILED DESCRIPTION 
     Current techniques for recognizing accented speech often are quite poor at recognizing speech when spoken with an accent. This disclosure describes techniques and apparatuses for recognizing accented speech using an accent library, and, in some embodiments, using different speech recognition correction levels based on an application field into which recognized words are set to be provided. 
     The following discussion first describes an operating environment, followed by techniques that may be employed in this environment, an example application having application fields, and proceeds with example apparatuses. 
       FIG.  1    illustrates an example environment  100  in which techniques for recognizing accented speech can be implemented. Example environment  100  includes a computing device  102  having one or more processors  104 , computer-readable storage media (storage media)  106 , a display  108 , and an input mechanism  110 . 
     Computing device  102  is shown as a smart phone having an integrated microphone  112  as one example of input mechanism  110 . Various types of computing devices and input mechanisms may be used, however, such as a personal computer having a separate, standalone microphone, a cellular phone connected to a pico-net (e.g., Bluetooth™) headset having a microphone, or tablet and laptop computers with an integrated stereo microphone, to name but a few. 
     Computer-readable storage media  106  includes an accent module  114 , device data  116 , mined data  118 , and applications  120 . Accent module  114  includes a linguistic library  122  and one or more accent libraries  124 . Accent module  114  may operate with, operate without, include, be integral with, and/or supplement a speech recognition engine (not shown). Accent module  114  is capable of recognizing accented speech, such as by determining, based on device data  116 , an accent library of accent libraries  124  to use to recognize speech in conjunction with linguistic library  122 . 
     Linguistic library  122  is associated with a language or dialect thereof, such as Australian English, American (US) English, British (King&#39;s) English. and so forth. Linguistic library  122  and a known speech recognition engine may operate to perform known speech recognition, though use of either or both is not required. Thus, accent module  114 , in some embodiments, uses one of accent libraries  124  to supplement a known speech recognition engine using a known type of linguistic library  122 . 
     By way of example, consider  FIG.  2   , which illustrates example linguistic libraries  122  and accent libraries  124  of  FIG.  1   . Here two example linguistic libraries, Australian English  204  and US English  206 , are shown. Associated with each of these linguistic libraries  204  and  206 , are numerous accent libraries  208  and  210 , respectively. 
     Accent libraries  208  include eight examples, though many more are contemplated by the techniques, including Australian (AU) English-Mandarin  208 - 1 , AU English-New South (N. S.) Wales  208 - 2 , AU English-New Zealand (NZ) Auckland  208 - 3 , AU English-NA Christchurch  208 - 4 , AU English-Scuba-Diver  208 - 5 , AU English-Outback  208 - 6 . AU English-Perth  208 - 7 , and AU English-Indonesia  208 - 8 . As is clear from the names, each of these accent libraries is associated with a large language group (Australian English) and accents present within that language group, whether it be recent Mandarin-speaking immigrants or persons involved in scuba diving. 
     Similarly, accent libraries  210  include eight examples, US English-Mandarin  210 - 1 , US English-Cantonese  210 - 2 , US English-Boston  210 - 3 , US English-Surfer  210 - 4 , US English-Hearing Impaired  210 - 5 , US English-Rural  210 - 6 , US English-South  210 - 7 , and US English-Alaska  210 - 8 . Note that the Mandarin accent libraries  208 - 1  and  210 - 1  can be different, as each is associated with a different linguistic library. There may, however, be some common elements between the accent libraries due to common traits of Mandarin speakers, whether speaking English in an Australian dialect or a US dialect Note that these accent libraries are nearly unlimited in number and in accents addressed. Regional accents, accents common to small or large immigrant groups, interests and subcultures, and even common physical characteristics, such as persons that are hearing impaired having some commonality in accent. 
     In the example of  FIG.  2   , each of accent libraries  124  contain supplemental information or algorithms for use by linguistic library  122 . Here linguistic library  122  is used for a large language group (e.g., that has more, an average, or a median for a larger number of persons), which is supplemented by one or more of accent libraries  124 . While this example of  FIG.  2    associates accent libraries with linguistic libraries, accent module  114  may forgo use of a linguistic library or a known speech recognition engine. Accent module  114  may instead provide its own algorithms and engine without use of other engines or libraries, relying instead on accent library  124  without linguistic library  122  but including algorithms or information useful for recognizing speech of a large number of persons. 
     Accent module  114  may determine which of accent libraries  124  to use to recognize accented speech based on device data  116  and/or mined data  118  (both of  FIG.  1   ). Device data  116  can include device personal data  126 , as well as data specific to computing device  102 . Data specific to computing device  102  can include the date of manufacture or purchase of computing device  102  (e.g., a recently-released mobile phone or tablet) and information about computing device  102 , such a manufacturer, hardware capabilities, and so forth. 
     Device personal data  126  includes data created or determined based on a user&#39;s interaction with computing device  102 , such as names of contacts, installed applications, receiving country or regions of messages, a user&#39;s name, contact information, non-standard keyboards (e.g., for a particular language other than the language for which computing device  102  is set), and contextual application information (e.g., search terms). Thus, names of contacts may indicate a country of origin of the user or a non-standard type of keyboard may indicate that a language other than the language setting for the computing device is the user&#39;s native language. Further, a receiving country or region for messages may include addresses in countries in which the language setting for the computing device is not a most-spoken language, e.g., the receiving country of Indonesia with a setting of Australian English, such as shown in  FIG.  2    with Australian English  204  and AU English-Indonesia  208 - 8 . 
     In more detail, emails or addresses in a user&#39;s contacts may indicate a nationality or ethnic origin of the user (e.g., Slavic first or last names). The addresses may indicate a native location of, or current location of, the user, as well as other details about the user that may be used to determine an accent library  124  for the user Names in email address lines or text in those emails may indicate the user&#39;s friends&#39; nationalities, origins, subcultures, or the user&#39;s business, or the user&#39;s interests. These interests, as noted further below, may indicate an accent, such as user&#39;s interest in surfing, scuba diving, or cooking. Some words and how these words are spoken can depend on these interests, and thus subcultures. 
     A person involved in scuba diving, for example, may use the terms “re-breather” and “Barotrauma,” which, but for an accent library associated with scuba diving, might be incorrectly recognized. Similarly, a person involved in surfing might use the terms “goofy foot,” “cutback,” or “closed out,” which might also be incorrectly recognized from a user&#39;s speech. Finally, for the cooking enthusiast, “La Creuset,” “rotisserie,” and “braising,” may be incorrectly recognized without the current techniques. 
     Device personal data  126  may also include other information useful in determining an accent and thus an accent library, such as Slavic-language books in the user&#39;s e-book library, Slavic-language news articles, articles and books about Poland, a saved weather channel for Warsaw, Poland, information about fishing in Estonia, a web search entry for accordion music, polka music in the user&#39;s music library, and so forth. 
     Mined data  118  may also or instead be used by accent module  114  to determine which of accent libraries  124  to use to recognize speech. Mined data  118  includes mined personal data  128 , which may include any personal data that may be found about a user of computing device  102 , either through the Internet or otherwise. Thus, mined personal data  128  may include the user&#39;s search terms, purchases, location, demographics, income, and so forth. 
     As noted, computer-readable storage media  106  also includes applications  120 , such as email application  130 , social network application  132 , or spreadsheet application  134  all of  FIG.  1   . Each of applications  120  includes one or more application fields  136 , which, in some embodiments, are used to determine a speech recognition correction level. By way of example, consider spreadsheet application  134 . Here a number-only cell  138  and a general-text cell  140  are each an example of application field  136 . Number-only cell  138  may require more-precise text than general-text cell  140 , and thus a different speech recognition correction level. 
       FIG.  3    illustrates example methods  300  for recognizing accented speech using an accent library determined based on device data. The order in which blocks of these and other methods are described is not intended to be construed as a limitation, and any number or combination of the described blocks in these and other methods herein can be combined in any order to implement a method or an alternate method. 
     At block  302 , device data is received for a computing device. Device data can be received responsive to an active retrieval performed at block  302 . Thus, using environment  100  of  FIG.  1    as an example, accent module  114  may retrieve device data  116  at block  302 , such as by searching contact data on computing device  102  and technological details about computing device  102 . 
     As noted in part above, device data  116  may include device personal data  126  and other, non-personal data associated with computing device  102 . By way of one ongoing example, assume that device data  116  indicates that computing device  102  is a smartphone released just 30 days ago that has significant computing power. This may be used in part to determine an appropriate accent library  124  based on demographics indicating that users of this smartphone, at least when recently released, are early adopters, technologically savvy, and aged between 18 and 32. 
     Assume that device personal data  126  includes contact names and addresses indicating a statistically relevant quantity of Asian last names and Asian first names. This statistical relevance can be determined in various manners, such as by comparison with a typical person&#39;s contact list that uses the same linguistic library  122 . Thus, while the average number of Asian first names for an American (US) English linguistic library user&#39;s contact list may be 1.3% and Asian last names 11% assume here that this user&#39;s contact list has 14% Asian first names and 29% Asian last names. Statistical analysis considers this statistically relevant based on it being one or more standard deviations from average. This indicates a likelihood that the user may not be a native English speaker or that family members of the user are likely not to be a native English speaker, especially the statistically-relevant quantity of Asian first names, as Asian first names are more likely to indicate a first-generation immigrant than Asian last names. 
     In addition to this information from a user&#39;s contact list, assume that device personal data  126  indicates that the user&#39;s name is “Molly Chin,” substantial numbers and durations of trips to the beach, a purchase of surfing gear, and that the user lives in southern California. 
     At block  304 , an accent library is determined based on the device data received. This accent library is determined for use in speech recognition. Continuing the ongoing embodiment, assume that accent module  114  correlates device data  116  with known accents associated with this type of device data, thereby determining that two different accent libraries  124  are likely, that of US English-Mandarin  210 - 1  and US English-Surfer  210 - 4  both of  FIG.  2   . Assume that the surfer accent library is determined to be more likely based on the young age projected for the user (as an early adopter and so forth), trips to the beach, an English first name (Molly), surfer-based purchases, and so forth In this ongoing example, accent module  114  determines accent libraries  124  based on device data  116 , though accent module  114  may also or instead base this determination on mined data  118  and information about prior speech received by computing device  102 . 
     At block  306 , speech is received at the computing device. Speech can be received in various manners, such as input mechanism  110  described above. Continuing the ongoing example, assume that the user says the following for entry into a text message to a friend “Jean, is it closed out?” 
     At block  308 , speech is recognized based on the accent library Concluding the ongoing example, accent module  114  uses, along with a speech recognition engine, linguistic library US English  206  and accent library US English-Surfer  210 - 4  selected based on device data as noted above. Here assume that, absent the accent library, that a speech recognition engine would recognize Molly&#39;s speech of “Jean, is it closed out?” as “Jean, is it close now?” Due to accent library US English-Surfer  210 - 4 , however, accent module  114  acts to correctly recognize Molly&#39;s speech as “Jean, is it closed out?” Accent module  114  then passes this text to the text field. 
     This recognition is due, in this example, to accent module  114  being able to select between multiple options for how to recognize Molly&#39;s speech, including an option that, but for the accent library, would have been considered a low-probability option for a current speech recognition engine relative to other likely options of “close now,” “hosed out.” and “closet.” Here accent library US English-Surfer  210 - 4  adds words, changes probabilities of words and phrases, and alters algorithms to change how certain sounds are interpreted (e.g., surfers have a different speech pattern, which is part of an accent, not just the words used). 
     Alternatively or additionally, methods  300  proceed to blocks  310  and/or blocks  312 - 318 . At block  310 , the accent library is updated based on corrected errors made during recognition of the speech. Block  310  may work in conjunction with, or separate from, methods  400  as described below. In the above example methods  300  correctly recognized Molly&#39;s speech. Were it incorrect, correction by the user (Molly Chin) can be recorded and used to update the accent library. 
     At block  312 , other speech is received at the computing device, the other speech received from a different speaker than the speech received at block  302 . By way of example, assume that Molly passes her smart phone to her father because she is driving. Assume that Molly asks her father to request a good Thai restaurant. Assume also that her father is a native Mandarin speaker and that English is a second language for him. Further, assume that, like many native Mandarin speakers, Molly&#39;s father uses tones to differentiate words, while English speakers use intonation (pitch patterns in sentences). Further, assume that Molly&#39;s father, like many Mandarin speakers, has problems pronouncing “I” sounds at the end of a syllable. Thus, Molly&#39;s father pronounces “why” as “wiw,” “fly” as “flew,” and “pie” as “piw.” Thus, when Molly&#39;s father asks the smart phone to find a Thai restaurant by saying “Find Thai Restaurant” but that, due to his accent, it sounds to a native US English speaker (or a speech recognition engine using only a US English library) as “Find Tew Restaurant.” 
     At block  314 , the other speech is dynamically determined not to be associated with the accent library determined at block  304 . Accent module  114  determines, in real time on receiving the speech “Find Tew Restaurant” that the speaker is not Molly and thus that accent library US English-Surfer  210 - 4  does not apply. Accent module  114  may determine this based on the “Tew” or other indicators, such as tonal variances within the word “Restaurant,” which is common to both Mandarin and Cantonese speakers, or simply that a history of speech received from Molly indicates that it is not Molly. This can be performed in numerous ways, such as Molly having a generally high-pitched voice and Molly&#39;s father not having this high pitch, speaking speed differences between Molly and Molly&#39;s father, and so forth. 
     At block  316 , another accent library, or no accent library, is determined for the other speech. Continuing this example, assume that accent module  114  determines, based on tonal variances within the word “Restaurant” that Molly&#39;s father is either a native Mandarin or Cantonese speaker. Further, assume that accent module  114 , determines that Molly&#39;s personal data indicates that she has friends and addresses associated more closely with a region of China in which Mandarin is the dominant language (e.g., Beijing) rather than regions associated with Cantonese (e.g., Hong Kong). This information may have already been determined at block  304  as noted above. 
     At block  318 , the other speech is recognized with the other accent library or with no accent library, as determined above. Concluding the ongoing example, accent module  114  recognizes Molly&#39;s father&#39;s speech of “Find Tew Restaurant” as “Find Thai Restaurant” by using accent library US English-Mandarin  210 - 1  of  FIG.  2    rather than incorrectly recognize this speech as “Find Two Restaurants.” 
       FIG.  4    illustrates example methods  400  for altering an accent library to more-accurately recognize accented speech. 
     At block  402 , a correction to a speech element is received. This correction corrects a speech element that was incorrectly recognized using an accent library. The correction can be received from a remote computing device, though this is not required. As noted in block  310 , speech recognition using an accent library may be incorrect and then corrected by a user. One or many corrections associated with an accent library can be received, such as from thousands of remote computing devices (e.g., smart phones, laptops, tablets, desktops and so forth). The computing device can be computing device  102  of  FIG.  1    but in this embodiment is a server computer remote from computing device  102  and at which corrections are recorded and accent libraries  124  are updated to improve recognition. 
     At block  404 , an accent library is altered to provide an updated accent library, the updated accent library able to more-accurately recognize the speech element. Using one of the above examples to illustrate, assume that the accent library US English-Mandarin  210 - 1  incorrectly recognized Molly&#39;s father&#39;s speech as “Find The Restaurant” instead of “Find Thai Restaurant.” Assume also that Molly&#39;s Father corrected the incorrect recognition to “Thai.” This correction, and many others like it for the same accent library, can be sent to, and received by, an updating entity. The updating entity can be accent module  114  on computing device  102 , or another accent module or other entity on a server computer. 
     At block  406 , the updated accent library is provided to the remote computing device or devices effective to enable the remote computing device or devices to more-accurately recognize the speech element. Thus, the speech element “Tew” will be more likely to be correctly recognized as “Thai” than “The” using the updated accent library. 
     Furthermore, device data can also be received from the remote computing device or devices that is associated with a user of the remote computing device and based on which the accent library was determined to be used for speech recognition of speech from the user. Thus, information about Molly for corrections to accent library US English-Surfer  210 - 4  or Molly&#39;s father for accent library US English-Mandarin  210 - 1  can be provided. 
     The update to the appropriate accent library may then be tailored to certain device data or other data. This, in effect, may act to provide sub-categories of accent libraries over time. Thus, a speaker, such as a person having similarities to Molly Chin may receive an update for US English-Surfer  210 - 4  based on her similarities in age (18-30) and region (Southern California) that another speaker using US English-Surfer  210 - 4  will not, such as a man (aged 45-60) living in a different region (Miami, Fla.). In so doing, updates can be provided to users based on whether the users or their computing devices have one or more same elements of device or mined data as the device or mined data of the remote computing device from which the correction was received. 
       FIG.  5    illustrates example methods  500  for recognizing speech at a speech recognition level based on an application field, which may use an accent library. 
     At block  502 , speech is received at a computing device. This can be as set forth in the various examples above. 
     At block  504 , a speech recognition correction level is determined based on an application field to which recognized text are set to be provided. One example of this can be example application fields  136  of  FIG.  1   , namely number-only cell  138  and a general-text cell  140  of spreadsheet application  134 . As noted above, accent module  114  may determine a speech recognition correction level based on the application fields, such as it likely needing highly accurate speech recognition or less accurate and/or faster recognition. 
     Consider, by way of example,  FIG.  6   , which illustrates an example email application&#39;s user interface  602  having application fields  604  and  606 . Application field  604  is an address field and application field  606  is a body field. Assume for example that Molly Chin from the above examples says “Surf Girl Seven Seven Seven At Gee Mail Dot Con.” 
     When opening a new email to send to a friend, assume that an email application will receive recognized text first into the email address field shown at application field  604 . When speaking, and after the email address is complete, assume the email application will receive recognized text into the body of the email, at application field  606 . In this example, accent module  114  determines that a maximum level of correction should be used for the address field. In such a case, accent module  114  uses an appropriate accent library  124  or makes other refinements that improve accuracy. Improving accuracy, however, can come at a cost in terms of time to recognize text and computing resources (processor and battery), to name but a few. Therefore, higher speech correction levels may not always be appropriate. 
     Note also that accent module  114  may apply different correction levels by determining to use none, one, or multiple accent libraries  114 , such as both a Mandarin and a Surfer accent library, for example. Further, accent module  114  may determine correction levels without use, or lack of use, of accent libraries  124 . For example, accent module  114  may use a different linguistic library  122  for some application fields or use an accent library  124  that is directed to spoken numbers rather than accents in normal speech Thus, one of linguistic libraries  122  may be directed to recognizing speech that is numerical or for addresses and another that is directed to recognizing speech that is conversational. In these and other ways set forth herein, the techniques may act to improve speech recognition. 
     At block  506 , the speech received is recognized at the speech recognition correction level to produce recognized text. Thus, for application field  604  (the email address field), accent module  114  recognizes speech at the determined speech recognition level, here at a maximum level using one or more accent libraries  124  and/or alternative linguistic libraries  122  directed to the expected speech. 
     At block  508 , recognized words and other text are provided to the application field Concluding the ongoing example for Molly Chin, at block  508  accent module  114  recognizes the speech of “Surf Girl Seven Seven Seven At Gee Mail Dot Com” not as words but, based on the accent library  124  and/or linguistic library  122 , as a combination of words and text, and also because it is an address field for an email, the “at” as the “@” symbol Thus, the speech is recognized as “surfgirl777@% GMail.com”. 
     While not required, the techniques, in some embodiments, use a less-than-maximum speech correction level when the application field is a body of an email, blog, social networking entry, or word-processing document. Conversely, the techniques, for address fields, number-only fields in spreadsheets, phone numbers, and so forth may use maximum speech correction levels and/or alternative linguistic libraries  122  or accent libraries  124 . 
       FIG.  7    illustrates various components of an example device  700  including accent module  114  including or having access to other modules, these components implemented in hardware, firmware, and/or software and as described with reference to any of the previous  FIGS.  1 - 6   . 
     Example device  700  can be implemented in a fixed or mobile device being one or a combination of a media device, computing device (e.g., computing device  102  of  FIG.  1   ), television set-top box, video processing and/or rendering device, appliance device (e.g., a closed-and-sealed computing resource, such as some digital video recorders or global-positioning-satellite devices), gaming device, electronic device, vehicle, and/or workstation. 
     Example device  700  can be integrated with electronic circuitry, a microprocessor, memory, input-output (I/O) logic control, communication interfaces and components, other hardware, firmware, and/or software needed to run an entire device. Example device  700  can also include an integrated data bus (not shown) that couples the various components of the computing device for data communication between the components. 
     Example device  700  includes various components such as an input-output (I/O) logic control  702  (e.g., to include electronic circuitry) and microprocessor(s)  704  (e.g., microcontroller or digital signal processor). Example device  700  also includes a memory  706 , which can be any type of random access memory (RAM), a low-latency nonvolatile memory (e.g., flash memory), read only memory (ROM), and/or other suitable electronic data storage. Memory  706  includes or has access to accent module  114 , linguistic libraries  122 , and accent libraries  124  and, in some embodiments, a speech recognition engine (not shown). 
     Example device  700  can also include various firmware and/or software, such as an operating system  708 , which, along with other components, can be computer-executable instructions maintained by memory  706  and executed by microprocessor  704 . Example device  700  can also include other various communication interfaces and components, wireless LAN (WLAN) or wireless PAN (WPAN) components, other hardware, firmware, and/or software. 
     Other examples capabilities and functions of these modules are described with reference to elements shown in  FIGS.  1  and  2   . These modules, either independently or in combination with other modules or entities, can be implemented as computer-executable instructions maintained by memory  706  and executed by microprocessor  704  to implement various embodiments and/or features described herein. Alternatively or additionally, any or all of these components can be implemented as hardware, firmware, fixed logic circuitry, or any combination thereof that is implemented in connection with the I/O logic control  702  and/or other signal processing and control circuits of example device  700 . Furthermore, some of these components may act separate from device  700 , such as when remote (e.g., cloud-based) libraries perform services for accent module  114 . 
     Although the invention has been described in language specific to structural features and/or methodological acts, it is to be understood that the invention defined in the appended claims is not necessarily limited to the specific features or acts described Rather, the specific features and acts are disclosed as example forms of implementing the claimed invention.