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Conclusions and Thoughts on Speech Recognition

Module by: Chris Pasich. E-mail the author

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Summary: The group's thoughts on our project.

Conclusions

Initially looking at the experiment, the plan was to have a text-dependent system, or a speaker verification system, or something that could actually determine what word was being spoken to the system by the user. It has become painfully clear that that would be a very difficult task to accomplish, and would require much more time, effort, and background on the subject than we could possibly acquire in a short period of time. Our system was, for what it did, relatively successful – it found vowels with regularity, and it identified speakers at a rate of almost 70% - a very good rate for a basic system.

As obvious, however, is how much more in depth speech recognition is than the scope of our project. Being able to determine what is spoken or who a speaker is with near perfect accuracy is an extremely formidable task. Preventing another individual from breaking into the system can be just as difficult, as it requires a system dependent on text and a system that will not accept anything other than what it specifies. Our initial idea of being able to determine what word was being spoken is, at best, naïve, and at worst not at all feasible. With that said, however, the end results were very acceptable.

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Definition of a lens

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A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

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Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

What are tags? tag icon

Tags are descriptors added by lens makers to help label content, attaching a vocabulary that is meaningful in the context of the lens.

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