Filler word detection through tokenizing and labeling of transcripts

A non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations comprising:

• acquiring tokens that are arranged in sequential order, wherein each of the tokens is representative of a different word in a transcript;
• supplying, to multiple models, the tokens and positional indexes of the tokens as input,
• wherein each of the multiple models is trained to learn a contextual parameter that is representative of a rule for identifying instances of a corresponding one of multiple filter words on a per-token basis, based on analysis of a preceding token, a succeeding token, or the preceding and succeeding tokens;
• and
• in response to a determination that at least two of the multiple models produce outputs indicating that a given token corresponding to a given word is representative of the corresponding filler word,
• determining which of the at least two models is trained to identify a longest string of words;
• and
• causing only the filter word identified by the determined model to be returned as a result.

A method comprising:

• acquiring a transcript that includes a series of words in sequential order as uttered in one or more audio files;
• tokenizing each word in the transcript as a separate token as to create a series of tokens that are arranged in sequential order;
• applying multiple machine learning models to the series of tokens,
• wherein each of the multiple machine learning models is trained to identify a different one of multiple filler words, each of which is representative of either a single word or multiple words that collectively define a phrase,
• wherein each of the multiple machine learning models is designed to take, as input, (i) the series of tokens and (ii) positional indexes of the series of tokens, and
• wherein each of the multiple machine learning models is designed to produce, as output, either (i) a zero response that indicates no tokens were determined to be representative of an instance of a corresponding filler word or (ii) an integer index that identifies locations of tokens, if any, determined to be representative of instances of the corresponding filler word;
• and
• posting, to an interface, the transcript in such a manner that words determined to be representative of the multiple filler words, based on outputs produced by the multiple machine learning models, are visually distinguishable from other words in the series of words.

A method comprising:

• acquiring a set of audio samples to be used to train a machine learning model to identify instances of a filler word,
• wherein each audio sample in the set of audio samples includes a spoken instance of the filler word;
• and
• providing the set of audio samples to a machine learning algorithm as input, such that the machine learning algorithm
• (i) derives, based on an analysis of the set of audio samples, a rule for identifying instances of the filler word based on context, wherein the rule specifies that
• (a) a form of punctuation must precede or succeed the filler word,
• (b) another instance of the filler word cannot precede or succeed the filler word, or
• (c) a word preceding the filler word cannot be a given part of speech, and
• (ii) produces, as output, the machine learning model that is trained to learn the rule.