Phonation Style Detection

Abstract

The invention provides a method for detecting phonation style in dynamic communication environments and making software control decisions based on phonation styles enabling an audio message to be classified based on the phonation style such as, but not limited to: normal phonation, whispered phonation, softly spoken speech phonation, high-level phonation, babble phonation, and non-voice sounds. The purpose of the invention is to introduce the phonation style as a way to control computer software.

Description

STATEMENT OF GOVERNMENT INTEREST[0001]The invention described herein may be manufactured and used by or for the Government for governmental purposes without the payment of any royalty thereon.BACKGROUND OF THE INVENTION[0002]Phonation is the rapid, periodic opening and closing of the glottis through separation and apposition of the vocal folds that, accompanied by breath under lung pressure, constitutes a source of vocal sound. Technology exists that detects sound created through phonation and attempts to understand and decode the sounds. Examples include Siri, automated phone menus, and Shazaam. Limitations on the current technologies mean that they only work well when the speaker is using a normal speaking or phonation style not including loud, babble, whisper, or pitch and they assume that the speaker wants to be heard and understood.[0003]Phonation style refers to different speaking styles which may include normal phonation, whispered speech phonation, low-level speech phonation...

AI Technical Summary

Benefits of technology

The invention provides a method for detecting different styles of speech and making decisions based on those styles. This allows for the classification of audio messages based on their phonation styles, such as normal phonation, whispered phonation, softly spoken speech phonation, high-level phonation, babble phonation, and non-voice sounds. The purpose of this invention is to introduce the phonation style as a way to control computer software. The method involves detecting speech activity, extracting features from the detected speech activity, and characterizing those features before making decisions based on those characteristics. This allows for faster and more accurate speech recognition and control in conversational environments.

Problems solved by technology

Limitations on the current technologies mean that they only work well when the speaker is using a normal speaking or phonation style not including loud, babble, whisper, or pitch and they assume that the speaker wants to be heard and understood.

The current state of the art has considered noise degraded speech applications where the goal is to extract the target speech or suppress the interfering speech, but lacks a decision making process of how to address different phonation styles.

Current methods also experience issues in situations of degraded speech which can occur in almost any communication setting.

Typically, speech degradation is assumed to be due to environmental noise or communication channel artifacts.

However, speech degradation can also occur due to changes in phonation style.

A speech processing algorithm that is trained using normally phonated speech but is given whispered or high-volume phonation style speech will quickly degrade and create nonsensical outputs.

In an unconstrained, dynamically changing environment, speech recognizers have not succeeded in being able to accurately recognize the spoken dialogue.

This process is complicated by multiple speakers, noisy environment, and unstructured lexical information.

The current state of the art lacks an approach for uniquely classifying the various phonation styles; it also does not address how to make appropriate follow-on decisions.

When there are multiple speakers or the speaker wishes to obfuscate his / her communication, the output of speech recognizers quickly degrades into non-sense lexical information.

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