Speaker Diarization via Audio Segmentation and Clustering
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Solution Overview
Problem
Conventional automatic speaker diarization techniques in call centers are resource-intensive and inaccurate, failing to effectively segment audio into speaker-specific segments for efficient processing by ASR engines.
Innovation Solution
The method involves preprocessing audio to remove non-speech portions, segmenting it into single-speaker segments using the Kullback-Leibler divergence measure for change point detection, and clustering these segments using mel frequency cepstral coefficients to separate speakers, employing a combination of modules like VAD, segmentation, and clustering within a call analytics server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speaker diarization techniques are used, then speaker separation is attempted, but accuracy deteriorates in noisy call center environments
Solution Approach 1:
The audio signal is divided into multiple segments using voice activity detection (VAD) to identify speech regions, and further segmented into speaker-specific segments through change point detection based on Kullback-Leibler divergence. This multi-level segmentation approach isolates speaker portions from noisy backgrounds and enables accurate diarization by processing manageable segments rather than entire noisy audio files.
2Measurement precision
If conventional speaker diarization techniques are used, then speaker segmentation is attempted, but computational resources are excessively consumed
Solution Approach 1:
Voice activity detection (VAD) is performed as a preliminary step to identify and isolate speech-containing segments before applying computationally intensive speaker segmentation algorithms. By pre-processing the audio to remove non-speech portions, the system reduces the amount of data requiring complex analysis, thereby lowering computational resource consumption while maintaining segmentation accuracy.
Solution Approach 2:
The audio is divided into manageable segments through VAD and change point detection, allowing speaker diarization algorithms to process smaller, discrete portions rather than entire audio files. This segmentation strategy reduces memory requirements and computational complexity while preserving speaker identification accuracy across the full audio duration.
3Measurement precision
If conventional speaker diarization techniques are used, then speaker separation is attempted, but processing time increases excessively
Solution Approach 1:
Voice activity detection (VAD) is performed beforehand to identify speech regions and remove non-speech portions before applying speaker segmentation algorithms. This pre-processing step reduces the total processing time by eliminating unnecessary computation on silent or noisy segments, while change point detection based on Kullback-Leibler divergence quickly identifies speaker transitions within the reduced dataset.
Data Source
AI summary
A method and an apparatus for automatic speaker diarization is provided. The method includes removing, at a call analytics server (CAS), non-speech portions from a call audio to produce a pre-processed audio, the call audio comprising speech from at least two speakers. Next, the pre-processed audio is divided, at the CAS, to multiple audio segments, each segment corresponding to speech from a single speaker of the at least two speakers. Next, the plurality of segments are clustered, at the CAS, into at least two groups corresponding the at least two speakers.