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Acoustic and linguistic features influence talker change detection.
Neeraj Kumar Sharma1, Venkat Krishnamohan1, Sriram Ganapathy1
1Learning and Extraction of Acoustic Patterns Lab, Indian Institute of Science, Bangalore.
The Journal of the Acoustical Society of America
|December 2, 2020
Summary
Human listeners better detect talker changes in familiar languages like English than unfamiliar ones like Chinese. Machine systems match human performance on familiar languages but lag on unfamiliar ones for talker change detection.
Area of Science:
- Speech processing
- Human-computer interaction
- Psychoacoustics
Background:
- Talker change detection is crucial for understanding multi-talker speech.
- Language familiarity impacts human speech perception.
- Evaluating machine performance against human capabilities is essential.
Purpose of the Study:
- To investigate the effect of language familiarity on human talker change detection.
- To identify acoustic features used by humans in this task.
- To benchmark machine diarization systems against human performance.
Main Methods:
- A listening test using English (familiar) and Chinese (unfamiliar) multi-talker speech.
- Analysis of miss rate, false-alarm rate, and response times (RT).
- Linear regression modeling of RTs with acoustic features; comparison with a machine diarization system.
Main Results:
- Language familiarity significantly affected miss rates, false-alarm rates, and RTs.
- Acoustic features were recruited for talker change detection.
- Machine systems achieved human parity for English but not for Chinese.
Conclusions:
- Human talker change detection performance is language-dependent.
- Machine diarization systems show limitations with unfamiliar languages.
- Further research is needed to improve machine performance on diverse languages.
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