Laughter Detection via Voiced-Unvoiced Burst Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current laughter detection systems in contact centers lack efficiency and accuracy, particularly in real-time processing and are not speaker-independent, failing to account for diverse environments, languages, and cultures.
Innovation Solution
A method and apparatus for detecting laughter episodes in audio signals by identifying bursts, extracting features, and determining compliance with predetermined thresholds, which includes a training phase to set these thresholds based on manually marked laughter episodes, using features like mean voiced length, mean duty cycle, and Itakura distance, and scoring components to assign certainty scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional laughter detection methods are used, then detection capability is achieved, but accuracy and reliability are insufficient for diverse speakers and environments
Solution Approach 1:
The audio signal is segmented into frames, with each frame further divided into voiced and unvoiced sections. This segmentation allows the system to analyze laughter at multiple granularities (frame-level and section-level), improving detection accuracy while maintaining speaker independence through consistent segmentation rules across different speakers.
Solution Approach 2:
The system extracts multiple acoustic parameters (mean voiced length, mean duty cycle, mean energy ratio, Itakura distance) and uses a scoring mechanism with adjustable thresholds. These parameter changes enable the system to adapt to different speakers and environments while maintaining accurate laughter detection through statistical analysis of parameter distributions.
2Speed
If real-time processing is implemented, then speed is improved, but detection certainty and accuracy may be compromised
Solution Approach 1:
The system performs preliminary voiced/unvoiced classification and burst detection on each frame before full laughter analysis. This preliminary action filters out non-laughter segments early, allowing real-time processing while maintaining high detection certainty through subsequent detailed feature extraction and scoring only on promising candidates.
Solution Approach 2:
The audio signal is processed in periodic frames with fixed duration (20-2000 samples), and laughter detection is performed periodically on each frame. This periodic processing enables real-time operation while accumulating sufficient statistical evidence across multiple frames to ensure detection reliability.
3Measurement precision
If comprehensive feature extraction is performed, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
Feature extraction is segmented into distinct stages: voiced/unvoiced classification, burst detection, and then feature extraction only on identified bursts. This segmentation reduces overall computational complexity by avoiding full feature extraction on all audio segments, while maintaining accuracy through focused analysis of relevant portions.
Solution Approach 2:
The system extracts only the most relevant features (mean voiced length, mean duty cycle, mean energy ratio, Itakura distance) from the audio signal, discarding redundant information. This selective extraction maintains high detection accuracy while reducing processing complexity by focusing computational resources on discriminative features.
4Reliability
If multiple thresholds and scoring criteria are used, then detection reliability is improved, but system complexity increases
Solution Approach 1:
The scoring component uses a universal set of thresholds and criteria that work across different speakers, languages, and environments. This universal scoring mechanism improves detection reliability by consistently applying the same reliable criteria, while the modular architecture keeps system complexity manageable through reusable components.
Solution Approach 2:
The system incorporates a training phase where thresholds are automatically determined based on manually marked laughter episodes, and includes performance measurement to evaluate detection quality. This feedback mechanism improves reliability by adapting thresholds to specific deployment contexts, while the automated nature of the process avoids manual complexity.
Data Source
AI summary
A method and apparatus for laughter detection. Laughter is detected through the presence of a sequence of at least a predetermined number such as three consecutive bursts, each burst comprising a voiced portion and an unvoiced portion. After detecting bursts, n-tuples such as triplets are detected, and a likelihood of each burst N-tuple to represent laughter is provided by comparison to predetermined thresholds. Finally, a total score is assigned to the signal based on the grades associated with the triplets and parameters such as the distance between the N-tuples, the total score representing the probability that the audio signal comprises a laughter episode. The method and apparatus preferably comprise a training step and module for determining the thresholds according to manually marked audio signals.