Dynamic ASR Model Tuning for Call-Center Transcription Accuracy
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Solution Overview
Problem
Conventional automatic speech recognition (ASR) systems for call-center audio transcription are prone to errors due to variations in speech patterns and background noise, leading to suboptimal conversion of conversational speech to text.
Innovation Solution
The system dynamically tunes the language and acoustic models of the ASR engine based on temporal and contextual information derived from previous audio segments, including keywords, emotion, speaking rate, and noise, to improve the accuracy of subsequent audio-to-text conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ASR systems are used for audio-to-text conversion, then the conversion process is simple and fast, but the transcription accuracy deteriorates due to speech pattern variations and background noise
Solution Approach 1:
The patent implements dynamic tuning of ASR engine parameters by continuously analyzing temporal and contextual information from audio segments. The system adjusts language model and acoustic model parameters in real-time based on detected speech patterns, speaker identity, and background noise characteristics, transforming the static ASR system into an adaptive one that maintains high accuracy across varying conditions
Solution Approach 2:
The system performs preliminary analysis of audio segments to extract temporal information (speech rate, pauses, rhythm) and contextual information (keywords, speaker identity, background noise) before conducting the main audio-to-text conversion. This preparatory processing enables the ASR engine to be pre-configured with optimal parameters for the upcoming transcription task, improving accuracy without adding significant complexity to the core conversion process
2Measurement precision
If the ASR engine is dynamically tuned based on temporal and contextual information, then the transcription accuracy is improved, but the processing time increases
Solution Approach 1:
The patent divides the audio conversation into multiple segments and processes them sequentially, extracting temporal and contextual information from each segment to tune the ASR engine for subsequent segments. This segmentation allows the system to perform dynamic tuning incrementally rather than processing the entire audio stream with full analysis, reducing the time penalty while maintaining accuracy improvements
Solution Approach 2:
The system implements a feedback loop where the output of each audio segment processing is used to refine the ASR engine configuration for the next segment. By continuously feeding back temporal and contextual information from previously processed segments, the system optimizes transcription accuracy progressively without requiring complete re-analysis of all audio data, thus limiting the increase in processing time
Data Source
AI summary
This disclosure relates generally to audio-to-text conversion for an audio conversation, and particularly to system and method for improving call-center audio transcription. In one embodiment, a method includes deriving temporal information and contextual information from an audio segment of an audio conversation corresponding to interaction of speakers, and input parameters are extracted from the temporal and contextual information associated with the audio segment. Language model (LM) and an acoustic model (AM) of an automatic speech recognition (ASR) engine are dynamically tuned based on the input parameters. A subsequent audio segment is processed by using the tuned AM and LM for the audio-to-text conversion.