International Business Machines Corporation (Armonk, NY)

Embodiments can be described as a method and system used to filter the speech of video. Speech in videos can contain offensive or profane words that need to be filtered. To ascertain whether a term or phrase is offensive, the contextual information from surrounding words and the contextual information from detected objects and scenes in the video are utilized. Words that are not wanted can be removed or collected and presented to the user.

The present application focuses on the method and system of filtering the speech of videos.

Speech can refer to words or phrases that are offensive, profane or otherwise offensive, vulgar or in any other way objectionable. The definition of what is objectionable differs from culture to culture, from language to language or between groups within the same culture, and among members of the same culture. In addition, terms or phrases that are considered objectionable in one context could be acceptable in a different context. Due to their many meanings, polysemantic terms are a problem. For instance, the term “bloody” in British English is considered an expletive that is objectionable. However, the word “bloody” is also used to describe blood loss such as that incurred by American baseball player Curt Schilling on his ankleduring Game 6 in the 2004 American League Championship Series, an incident which is often referred to as the “bloody sock.” The mention of Curt Schilling’s bloody sock is not considered to be profane or objectionable. It is difficult to detect offensive speech due to culture or language, context, individuals, and the differences in context.

The past was when speech filtering systems depended on lists of vulgar words that were kept in “dictionaries” to evaluate against the speech being filtering. These lists can be too broad and do not include certain words. This is especially difficult for polysemantic words, such as “bloody”. The above example illustrates how “bloody” is filtered to mean Curt Schilling’s socks, even though it’s not considered to be a profane word. Filtering “bloody” from the expression “bloody socks” is problematic since it eliminates an important piece of information that is valid and unobjectionable that results in a loss or meaning to the expression.

Video speech allows for better filtering of offensive phrases and words. It also gives the context of the speech by providing visual information about the scene where the speech is taking place. Photos of bloody socks during a baseball match could suggest that “bloody”, as an expletive is not being used. Traditional dictionary-based systems do not use video content to determine context. Another problem with conventionaldictionary systems is that they are relatively inflexible because they depend on a user updating the dictionary database of words. More flexible and contextually aware speech recognition systems that recognize speech in video are beneficial, as they provide moreaccurate filtering of offensive speech that blocks content, and at the same time allowing legitimate speech to be conveyed to the user/viewer.

Embodiments can provide a computer implemented method in a data processing system comprising a processor and a memory comprising instructions, which are executed by the processor to cause the processor implement speech filtration of a video, themethod comprising converting speech in a scene of the video to text, wherein the text comprises words; evaluating the text to determine candidate words to filter; for each identified candidate word: determining if there is a non-objectionable meaning tothe identified candidate word, and if there are no non-objectionable meanings to the identified candidate word, then filtering the candidate word from the speech; for each candidate word having at least one non-objectionable meaning: identifyingsurrounding words in the scene, calculating a first context for the candidate word using semantic analysis of the surrounding words, receiving a list of objects in the scene, calculating a second context for the candidate word using semantic analysis ofthe list of objects, determining if the candidate word is objectionable based on the first and second contexts, and if the candidate word is objectionable, then filtering the candidate word from the speech.

Embodiments also offer a method further comprising wherein the process of evaluating the text in order to determine potential words to filter is done by using a pre-filter to exclude words that are not designated as potential words.

Embodiments may also include removing punctuation.

Embodiments may also provide an additional method that incorporates the following: analyzing the text in order to determine candidate words to filter, determining the language of the text, choosing a dictionary based on the languageof the text and comparing each word to the dictionary selected.

Embodiments can also be an additional method of ensuring that the scene includes a portion that has a stop and start time.

Embodiments could also offer an additional method for the recognition of the surrounding words. This involves identifying the words that lie between the word being considered and the subject.

Embodiments can further provide a method further comprising wherein the semantic analysis is an analysis using deep learning.

Embodiments also offer a method further comprising wherein the objects in the scene comprise an image of textual description of a visual element.

Embodiments may also provide an option that includes filtering, which includes muting the voice.

Embodiments may also provide the opportunity to add each candidate word from the dictionary.

A computer-readable program can also be supplied that comprises the computer-readable medium along with a computer-readable program. The computer readable program is executed by a processor, causes the processor toperform various ones of, or combinations of, the operations that were described previously in connection with the method illustrative embodiment.

A system may also be provided in another embodiment. A persistent identification processor may be an element of the system. It may perform a variety of operations, as well as combinations, of those previously mentioned in relation to the method illustrated embodiment.

Additional features and advantages of the disclosed invention will become clear from the detailed description of the illustrative embodiments which proceeds with reference to the drawings that accompany it.

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