Domain-Specific Speech Recognition Training for Accented Audio
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Solution Overview
Problem
Existing ASR models struggle to accurately recognize domain-specific audio and speech with heavy accents, often resulting in low transcription quality.
Innovation Solution
A system that analyzes audio using a generic ASR model to generate timestamps and confidence scores, incorporates human corrections, and trains the model on domain-specific data to create specialized ASR models with improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generic ASR model is used to transcribe audio, then the model can handle diverse speech patterns, but transcription accuracy deteriorates for domain-specific and accented speech
Solution Approach 1:
The system creates specialized ASR models tailored to specific domains (medical, legal, technical) and accent types rather than using a single generic model. Each domain-specific model is trained on targeted training data and fine-tuned using domain-specific audio files, enabling high accuracy for that particular domain while maintaining the ability to handle diverse speech patterns through multiple specialized models.
Solution Approach 2:
The system modifies model parameters by training on domain-specific training data and fine-tuning with domain-specific audio files. This changes the model's internal parameters to better capture domain-specific vocabulary, terminology, and accent patterns, thereby improving transcription accuracy for that domain while maintaining versatility through the creation of multiple specialized models.
2Measurement precision
If a specialized ASR model is trained for a specific domain, then transcription accuracy improves for that domain, but the model's ability to handle diverse speech patterns deteriorates
Solution Approach 1:
The system creates a multi-functional ASR service that can handle multiple domains and accent types by maintaining a library of specialized models. The system determines which specialized model to use based on the audio file's characteristics (domain type, accent detection), allowing a single system to universally handle diverse speech patterns across multiple domains while each individual model maintains high accuracy for its specific domain.
Solution Approach 2:
The system dynamically selects and switches between different specialized ASR models based on the input audio's characteristics. Rather than using a static single model, the system adapts its behavior by selecting the most appropriate domain-specific or accent-specific model for each transcription task, thereby maintaining both accuracy and versatility.
3Ease of operation
If existing ASR models are used without domain-specific training, then the system remains simple to operate, but transcription quality deteriorates for domain-specific audio
Solution Approach 1:
The system automatically determines which specialized model to use by analyzing the audio file's characteristics (domain type, accent detection) without requiring user intervention to select models manually. The system self-selects the appropriate domain-specific or accent-specific model based on pre-computed features, maintaining ease of operation while delivering high transcription quality through automated model selection.
Solution Approach 2:
The system pre-computes and stores domain labels and accent information from audio files before transcription occurs. This preliminary analysis of audio characteristics enables the system to automatically select the appropriate specialized model in advance, maintaining simple user operation while ensuring high transcription quality through pre-prepared domain-specific models.
Data Source
AI summary
Systems, methods, and computer-readable storage media for speech recognition, and more specifically to training domain or accent-specific speech recognition language models. The system can receive a starting ASR (Automated Speech Recognition) neural network model configured to receive audio in the language and generate a starting transcript of the audio. The system can also receive a request to generate a specialized version of the starting ASR neural network model. Based on the request, the system can execute an analysis of the audio, resulting in timestamps and a confidence of each word in the audio. The system can then execute the starting ASR neural network model, resulting in a starting transcript. The system can then generate training data using the timestamps, confidences, and the starting transcript, and train an updated ASR based on the training data, resulting in the specialized version of the starting ASR neural network model.