Speaker Diarization via Pause Detection and LLM Context
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Solution Overview
Problem
Current speech-to-text conversion technologies face challenges in accuracy due to environmental noise, language dialects, regional accents, and individual pronunciation variations, leading to error-laden text inputs that result in misleading insights, particularly in segmenting streamed or recorded speech audio.
Innovation Solution
An apparatus and method that utilize a combination of pause detection techniques and speaker diarization to divide speech audio into segments based on likely sentence pauses and speaker changes, employing multiple threads to process audio data, and using acoustic and language models to generate transcripts, with relative weighting to account for noise susceptibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech audio is segmented using traditional methods, then processing speed is maintained, but segmentation accuracy deteriorates due to noise and dialect variations
Solution Approach 1:
The speech audio is divided into multiple segments based on detected pause points and speaker changes. Each segment is processed independently through the speech-to-text conversion pipeline, allowing for more accurate transcription of individual segments while maintaining overall processing efficiency. The segmentation is performed by detecting pause points (where silence exceeds a threshold duration) and speaker change points (where acoustic features indicate a different speaker), creating natural boundaries for transcription segments.
Solution Approach 2:
Acoustic models and language models are introduced as intermediary components between the raw speech audio and the final text output. The acoustic model converts audio features into phonetic representations, while the language model refines these into meaningful text. These intermediary models help bridge the gap between noisy speech inputs and accurate text outputs, improving segmentation accuracy without requiring direct complex processing of the entire audio stream at once.
2Measurement precision
If multiple pause detection techniques are combined, then segmentation accuracy improves, but computational resources increase
Solution Approach 1:
The system dynamically adjusts which pause detection techniques are applied based on the characteristics of the speech audio being processed. For clear, noise-free speech, simpler pause detection methods suffice. For noisy or dialect-heavy speech, the system activates additional detection techniques with higher computational cost. This dynamic adaptation allows the system to optimize the balance between accuracy and computational energy consumption in real-time.
Solution Approach 2:
The speech-to-text system performs self-adjustment by monitoring the quality and characteristics of its own processing outputs. When transcription accuracy drops below thresholds (indicating noisy input or dialect challenges), the system automatically activates additional pause detection techniques and processing passes without external intervention. This self-service mechanism ensures accuracy maintenance while avoiding unnecessary computational expenditure on clear, straightforward speech segments.
Data Source
AI summary
A system, method, and computer-program product includes receiving speech audio of a multi-turn conversation, generating, via a speech-to-text process, a transcript of the speech audio, wherein the transcript of the speech audio textually segments speech spoken during the multi-turn conversation into a plurality of utterances, generating a speaker diarization prompt that includes contextual information about a plurality of speakers participating in the multi-turn conversation, inputting, to a large language model, the speaker diarization prompt and the transcript of the speech audio, and obtaining, from the large language model, an output comprising an enhanced transcript of the speech audio, wherein the enhanced transcript of the speech audio textually segments the speech spoken during the multi-turn conversation into a plurality of refined utterances and associates a speaker identification value with each of the plurality of refined utterances.