Notes: Key Areas for Developing a General Language Model

Below is a list of the key areas in developing a general language model:

  • Interoperability between Voice Recognition SDKs [coding for developers to build upon]
  • Language agnosticism [two types English, non-native]
  • Recommendation for language models [speech corpus, usage/context type]
  • Corpus refers to the things that make up a speech “database”
  • Accuracy
  • Acoustic models [mathematical formulas to help computer figure out what was said]
  • Techniques for audio files and trainer [combination of quality of sound, reduced noise, programming specialized in “training” computer’s recognition]
  • Tolerance variables
  • Library quality
  • Identify and ability to modify accuracy model variables
  • Library robustness
  • Speech databases
  • Size
  • Speech corpora [with raw audio files]
  • Read speech
  • Spontaneous speech
  • Text transcriptions
  • Speed and responsiveness of Voice Recognition SDK
  • Hardware constraints
  • Network architecture constraints [application ability and performance extension]
  • Library constraints
  • Size
  • Usage/Context Type [industry/role based/situation specific]
  • Quality

I am a technologist with a strong background in software engineering. I have many interests. My current distractions are 70s-80s-90s music [it's a very eclectic collection], ontology, information architecture, mobile device technology, medical bioinformatics, artificial intelligence, and nanorobotics.

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