A graph-theoretic approach to identifying acoustic cues for speech sound categorization

Area of Science:

• Speech processing
• Acoustic phonetics
• Computational linguistics

Background:

• Human speech recognition relies on mapping acoustic cues to phonemes.
• Talker variability in acoustic cues presents a challenge for speech recognition models.
• Existing models struggle to balance cue variability with specific phoneme mappings.

Purpose of the Study:

• To develop a method for characterizing speech information that accounts for talker variability.
• To simplify acoustic cue spaces for phoneme categorization.
• To identify the most relevant acoustic cues for specific phonemes.

Main Methods:

• Utilized graph theory to model connections between talkers and acoustic cues.
• Identified relevant subgraphs within these networks to reduce cue dimensions.
• Developed classifiers based on identified cue subsets.

Main Results:

• The graph-based approach successfully reduced the space of acoustic cues.
• Subgraphs captured essential cues for phoneme categorization across talkers.
• Classifiers trained on reduced cue sets performed comparably to those using all cues.

Conclusions:

• Graph theory offers an effective way to model and simplify acoustic cue information in speech.
• This method reduces model complexity while preserving essential information for speech perception.
• The findings provide insights into the crucial acoustic cues for recognizing speech sounds.