An approach to modifying text captioning is provided, comprising receiving, by a captioning modifier program, input data associated with a video stream, analyzing, by the captioning modifier program, the input data, altering, by the captioning modifier program, text captioning associated with the video stream to indicate eventful aspects based on an analysis of the input data and generating, by the captioning modifier program, supplementary information associated with the video stream based on the analysis and providing the supplementary information as an addition to the text captioning.

BACKGROUND

The present invention relates generally to the field of text captioning, and more particularly to cognitive analysis of text captioning.

Text captioning is the addition text to a video stream, e.g., a live or prerecorded video stream on television (TV) or a communication network, such as the Internet. Text captioning, including closed captioning, subtitles, and the like, can be used to present character dialogue and contextual information associated with the events of a video stream in real time. As an accessibility feature, text captioning is useful for those with disabilities or impairments (such as deafness) or otherwise a useful feature for viewers in a noisy environment who may have difficulty hearing the audio associated with a video stream.

Cognitive computing refers to computer processing that simulates human thinking and cognition processes. Cognitive computing involves techniques such as data mining, pattern recognition, machine learning, natural language processing and the like to analyze input data and discover new insights and solutions. With the more input data that is processed, the computational processes used to analyze input data by cognitive computing systems can become more refined and effective. Cognitive computing systems can find application in the field of text captioning in video streams for enhancing formatting and improving the quality of the information conveyed through text captioning.

SUMMARY

According to one embodiment of the present invention, a method for modifying text captioning is provided, the method comprises receiving, by a captioning modifier program, input data associated with a video stream; analyzing, by the captioning modifier program, the input data; altering, by the captioning modifier program, text captioning associated with the video stream to indicate eventful aspects based on an analysis of the input data; and generating, by the captioning modifier program, supplementary information associated with the video stream based on the analysis and providing the supplementary information as an addition to the text captioning. A corresponding computer program product and computer system are also disclosed herein.

DETAILED DESCRIPTION

Embodiments of the present invention recognize text captioning (e.g., closed captioning, subtitles, etc.) provided by streaming media content providers is often limited in formatting and structure. For example, the information is often displayed along the bottom edge of the screen and only depicts at most two rows of text at a time, typically in a fixed font and size. While text captioning generally enhances the viewing experience of those with hearing impairments or in noisy environments, embodiments of the present invention acknowledge that text captioning can be improved via real-time processing to further enhance viewer experience and comprehension.

Viewer experience would be greatly enhanced with real-time image and audio processing that would be able to capture emotions, changes in volume, or other important aspects of scenes in a video stream in order to modify the text captioning in a meaningful way, so viewers can have a better understanding of what is being portrayed on screen. Further, the text captioning in a video stream may be blocking important aspects or images on the screen. Thus, an intelligent system with the capability to dynamically move text captioning about the screen to avoid this would also be a useful feature. Embodiments of the present invention therefore provide a solution to intelligently, automatically modify text captioning to address these issues, among others.

In describing embodiments in detail with reference to the figures, it should be noted that references in the specification to “an embodiment,” “other embodiments,” etc., indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, describing a particular feature, structure or characteristic in connection with an embodiment, one skilled in the art has the knowledge to affect such feature, structure or characteristic in connection with other embodiments whether or not explicitly described.

The present invention will now be described in detail with reference to the figures.FIG. 1is a functional block diagram illustrating a distributed data processing environment100, in accordance with one embodiment of the present invention. Distributed data processing environment100comprises viewing device102and content server140, interconnected over network120.

Viewing device102can be a laptop computer, tablet computer, netbook computer, personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smart phone, a smart television (TV) or any programmable electronic device capable of communicating with content server140via network120. Viewing device102comprises video streaming application103and captioning modifier104for modifying text captioning to enhance viewer experience and comprehension. Video streaming application103can be, but is not limited to, a web browser or a streaming media service application used to watch streaming media content, e.g., movies, videos, TV shows and the like, on demand. Video streaming application103provides the accessibility feature of text captioning such as, but not limited to, closed captioning or subtitles, for providing textual information associated with video streams that are viewed. In some embodiments, video streaming application103comprises captioning modifier104, i.e., captioning modifier104is built into video streaming application103.

Style modifier108is a component which communicates with input analyzer106to alter the style and/or format of text captioning. Information generator110is a component which communicates with input analyzer106to generate and display additional textual information for improving user comprehension of streaming video content. Captioning relocator112is a component which communicates with input analyzer106to dynamically move, i.e., relocate, text captioning about a display screen associated with viewing device102. This text captioning relocation provides a mechanism for improving viewer experience by moving text captioning to parts of the display screen which minimize the extent to which the text captioning blocks important aspects of a scene at any given time.

In various embodiments of the present invention, content server140can be a laptop computer, tablet computer, netbook computer, personal computer (PC), a desktop computer or any programmable electronic device capable of communicating with viewing device102via network120. Content server140represents a system used to provide and distribute content, e.g., streaming media content viewable via video streaming application103.

Network120can be, for example, a local area network (LAN), a wide area network (WAN) such as the Internet, or a combination of the two, and can include wired, wireless, or fiber optic connections. In general, network120can be any combination of connections and protocols that will support communications between viewing device102and content server140.

Viewing device102may include internal and external hardware components, as depicted and described in further detail with respect toFIG. 4.

FIG. 2is a flowchart200depicting operational steps of captioning modifier104, in accordance with an embodiment of the present invention. The illustrative example ofFIG. 2is provided to facilitate discussion of aspects of the present invention, and it should be appreciated thatFIG. 2provides only an illustration of an embodiment of the present invention and does not imply any limitations with regard to the variations or configurations in which different embodiments may be implemented.

Input analyzer106receives audio and/or visual input data, associated with a video stream, from video streaming application103(step202). The audio and/or visual input data can be any sounds (e.g., character dialogue and other noises) and images associated with the unfolding events and scenes of the video stream. Input analyzer106, embodied as a cognitive computing system, analyzes the received audio and/or visual input data in real-time (step204). The real-time analysis of video stream input data is performed to intelligently modify text captioning associated with the video stream, provided the text captioning feature is currently enabled by the viewer. The intelligent modification of text captioning by input analyzer106can be based, at least in part on viewer-specified preferences, or entirely based on default settings.

Input analyzer106runs a model that has been trained with the ability to identify objects and human actors (e.g., pre-trained models are available with deep learning frameworks such as Berkeley Vision and Learning Center (BVLC) Caffe or International Business Machines Corporation's (IBM) Watson). For each video frame or group of frames, input analyzer106detects the default caption positioning and relative position of captions and nearby objects. Input analyzer106also has a model to comprehend speech. If by speech analysis it is determined that captions are hiding an object from view and those objects were being referred to in the speech, input analyzer106can provide downstream alerts and events (e.g., to captioning relocator112) so that the caption modification and repositioning may take place.

Input analyzer106also has a model to analyze the increasing amplitude of speech. When it detects that speech is increasing in amplitude, it has the ability to parse speech using NLP (natural language processing) models that can provide parts-of-speech taggers to identify words in speech that need highlighting or special treatment (e.g., increased font size). Input analyzer106processes video frames to understand object types and object locations. Object types are usually inferred using standard deep learning models such as AlexNet, ResNet and VggNet. Object position is usually captured in the form of a graph with relative pixel positions.

Input analyzer106processes speech content and speech amplitude so that caption related adjustments and optimizations can take place. Such speech models are available with IBM Watson API (application program interface) and may be deployed in the path of live video streams for suitable processing. Pre-trained speech models are usually deployed in the path of incoming streams.

Style modifier108alters the style of text captioning associated with the video stream based on the analysis of the audio and/or visual input data to improve viewer comprehension (step206). Input analyzer106analyzes the video stream in real-time, detecting eventful aspects such as character emotions, volume changes and the like. Based on such analysis, style modifier108alters stylistic characteristics of portions of the text captioning to reflect and indicate these and other such aspects of the video stream, e.g., the font of the text captioning is bolded or italicized, the text size increases or decreases, the text color changes, etc.

As one example, for given changes in sound volume, either in a subject's voice or background noise, the text size is altered to indicate such changes. Therefore, as the sounds get louder, text size will increase (i.e., text corresponding to the sounds), and when sounds get quieter, text size will decrease. Similarly, if a character is yelling in a scene of the video stream, the corresponding text can be presented in all capital letters and at a larger size than usual, or, if they are whispering, the text size is presented as smaller than usual. If a character is placing particular emphasis on something they are saying or it is detected that they are saying something that is highly significant relative to the plot, the corresponding text can be italicized by style modifier108. Further, the font type (e.g., Arial, Times New Roman, etc.) can be changed for a character who is speaking with an accent.

Captioning modifier104can provide one or more user interface (UI) components for enabling viewers to configure settings associated with text captioning modification. For example, a viewer of the video stream may configure the settings to have style modifier108change the text color to red or another color to indicate when a detection is made that a character is speaking angrily.

Information generator110generates and displays supplementary textual information, based on the real-time analysis of the video stream, to improve viewer comprehension (step208). The supplementary information is appropriately added to the text captioning being displayed and can be information related to the context of a scene. For example, information generator110can add text to the text captioning which indicates the color of a certain object in a scene to which a character is speaking about, a more detailed example of which will be depicted and described subsequently with respect toFIG. 3A. This type of supplementary information added to text captioning can greatly improve the comprehension of viewers affected by color-blindness or other similar impairments.

Additionally or alternatively, supplementary information can be added which describes the relationship between two or more characters, or describes objects or events being spoken about, thus providing context to the scene to those who are just tuning in or who are otherwise unfamiliar with the plot and events associated with the video stream. Further, supplementary information added to the text captioning of a video stream can be information describing certain sounds which are not otherwise indicated by the standard, unmodified text captioning provided by a content provider. For example, text captioning is added to indicate that strong wind can be heard blowing in a scene and some shutters are banging against the side of a house.

In some embodiments, supplementary text added by information generator110is presented in a different size and/or font style (e.g., italicized, bold, etc.) than usual to indicate that it is supplementary information which has been added. Users of captioning modifier104can configure custom settings to distinguish the different text size and/or font style associated with supplementary information (added by information generator110) from that of altered text sizes and/or font styles associated with the eventful aspects of a scene (e.g., character emotions, volume changes, etc.), which are altered by style modifier108. Additionally, users may configure settings to specify which types of events, images or sounds (associated with a video stream) should be included as supplementary information and/or the frequency with which supplementary information is added to the text captioning.

Further, in some embodiments of the present invention, information generator110has the ability to provide supplemental information even if a viewer has text captioning disabled. Information generator110can project supplemental information in the form of text captioning to the screen to aid in understanding, even if text captioning is currently disabled. This ability would be useful in cases when a character mumbles, becomes very quiet or silently annunciates something, for example. The system detects these changes in real time and projects either determined dialogue (i.e., an estimation of what the character has said) or actual speech, depending on the scenario, as text captioning. This ability can work in conjunction with user specified preferences.

Captioning relocator112dynamically repositions text captioning associated with a video stream (step210). The repositioning of text captioning is based on an analysis of where the text captioning is located on a display screen during a given scene of the video stream. Repositioning the text captioning is performed to minimize the extent to which the text captioning is blocking key visual aspects of the video stream, aspects which are significantly related to viewer comprehension of the events of an unfolding scene. Some examples of key visual aspects can be, but are not limited to, character's faces and/or bodies, scoreboards, objects in motion, visible objects being spoken about or the like.

Based on a determination by input analyzer106that the text captioning is at least partially obscuring one or more key visual aspects of a video stream, input analyzer106determines another location within a display window of the video stream where the text captioning can be repositioned to. The another location to which the text captioning can be repositioned can be a location within the video stream where there are no key visual aspects for the text captioning to block or a location where the extent to which the text captioning will be obscuring one or more key visual aspects, relative to its current position in the video stream, will be reduced. When input analyzer106has determined another location, input analyzer106communicates with captioning relocator112to indicate that the text captioning should be relocated, and captioning relocator112subsequently repositions the text captioning to the another location.

Input analyzer106can utilize a predetermined threshold for determining whether or not text captioning should be repositioned. For example if input analyzer106detects that text captioning associated with a video stream is covering more than half (i.e., 50%) of one or more key visual aspects of a scene, input analyzer106determines another location within the video stream to move the text captioning to. In some embodiments, input analyzer106determines a portion of the whole video stream (visible in a display window) which has few or no key visual aspects, such as a portion significantly comprising background scenery, e.g., 75% or more. Input analyzer106communicates with captioning relocator112to have the text captioning repositioned to the determined portion of the video stream. Further, in some embodiments, captioning modifier104enables users to manually reposition text captioning in a video stream with a provided UI component. Users can accomplish a manual reposition of text captioning using any suitable means of control, such as, but not limited to, directional buttons on a remote control, mouse clicks, key strokes, touching a touch screen device, etc.

FIG. 3Aillustrates example modified text captioning300, in accordance with an embodiment of the present invention. In a video stream to which modified text captioning300corresponds, a character is asking “Where did the green hat come from?” To a viewer with color-blindness, the hat to which the character is referring may not be visually obvious and/or there may be more than one hat visible in the related scene. Information generator110, having communicated with input analyzer106, has added the following supplementary information, presented in parentheses and italicized, to the text captioning: (The hat on the table at right is a light green color). This supplementary information has been added to enhance viewer comprehension of the scene by directing viewers reading the text captioning to the location of the specific object being referred to, i.e., the green hat.

FIG. 3Bdepicts a streaming sporting event320where text captioning has been repositioned, in accordance with an embodiment of the present invention. Streaming sporting event320is a baseball game being viewed in a display window on a display screen associated with viewing device102, streamed via video streaming application103. Visible in the display window are text captioning322, home plate324, pitcher's mound326and scoreboard328. Input analyzer106has received visual input data associated with the images comprising streaming sporting event320, analyzed the scene and made a determination that text captioning322should be relocated. For example, text captioning322may have previously been located along the bottom of the screen, at least partially obscuring pitcher's mound326and/or scoreboard328(not depicted).

Captioning relocator112receives communication from input analyzer106to move text captioning322. Captioning relocator112subsequently repositions text captioning322on the left side of a display window displaying streaming sporting event320, as depicted. This is done so that text captioning322does not visually block key visual aspects of streaming sporting event320, e.g., home plate324, pitcher's mound326and scoreboard328, thus preventing an interference to viewer comprehension of the events of the scene. It should be noted that in some embodiments, users can configure settings to control the frequency with which text captioning is repositioned.

Viewing device102includes communications fabric402, which provides communications between cache416, memory406, persistent storage408, communications unit410, and input/output (I/O) interface(s)412. Communications fabric402can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric402can be implemented with one or more buses or a crossbar switch.

Memory406and persistent storage408are computer readable storage media. In this embodiment, memory406includes random access memory (RAM). In general, memory406can include any suitable volatile or non-volatile computer readable storage media. Cache416is a fast memory that enhances the performance of computer processor(s)404by holding recently accessed data, and data near accessed data, from memory406.

Captioning modifier104can be stored in persistent storage408and in memory406for execution by one or more of the respective computer processors404via cache416. In an embodiment, persistent storage408includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage408can include a solid state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.

Communications unit410, in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit410includes one or more network interface cards. Communications unit410can provide communications through the use of either or both physical and wireless communications links. Captioning modifier104can be downloaded to persistent storage408through communications unit410.

I/O interface(s)412allows for input and output of data with other devices that can be connected to viewing device102. For example, I/O interface412can provide a connection to external devices418such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices418can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., captioning modifier104, can be stored on such portable computer readable storage media and can be loaded onto persistent storage408via I/O interface(s)412. I/O interface(s)412also connect to a display420. Display420provides a mechanism to display data to a user and can be, for example, a computer monitor.