ASR Training via TTS Morphing for Speech Pattern Adaptation
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Solution Overview
Problem
Current speech recognition systems face challenges in achieving high accuracy due to variations in human speech patterns, particularly in pitch, duration, and prosody, which existing Automatic Speech Recognition (ASR) engines struggle to adapt to effectively.
Innovation Solution
The proposed solution involves training an optimized ASR engine using a text-to-speech (TTS) engine, where input speech is morphed to match the audio output of the TTS engine, and utilizing speaker-dependent training to fine-tune recognition, combining morphing of speech units with TTS training to create a near-perfect acoustic model for improved recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional ASR engines are used to recognize varied human speech patterns, then the system can process diverse speech inputs, but recognition accuracy deteriorates due to variations in pitch, duration, and prosody
Solution Approach 1:
The patent creates synthetic speech samples by copying and transforming text-to-speech (TTS) audio outputs to generate training data. The ASR engine is trained on these synthesized samples where the ground truth is known, enabling the system to learn optimal speech patterns without requiring extensive manual annotation of diverse real-world speech data.
Solution Approach 2:
The system applies parameter transformations to TTS audio signals, including pitch shifting, duration adjustment, and prosody modification. These parameter changes create varied training samples from a single TTS source, enabling the ASR engine to learn robust speech recognition across different speech characteristics while maintaining high accuracy.
2Measurement precision
If speaker-dependent training is implemented to improve accuracy for specific speakers, then recognition precision improves, but system complexity increases due to additional training requirements
Solution Approach 1:
The system performs self-training by automatically generating training data from TTS outputs without requiring manual speech collection or annotation. The ASR engine trains on synthetically generated samples where the correct transcription is inherently known, eliminating the need for external linguists or manual data preparation processes.
Solution Approach 2:
The system performs preliminary training using synthesized speech data before deploying the ASR engine for actual speech recognition. This pre-training phase establishes a strong baseline model that can be further fine-tuned with speaker-specific data if needed, reducing the complexity of on-the-fly adaptation.
3Measurement precision
If more training data is collected to improve recognition accuracy, then transcription precision improves, but time and resource consumption increase
Solution Approach 1:
Instead of collecting extensive real-world speech data, the system copies and transforms TTS audio outputs to generate large volumes of training samples. This synthetic data generation approach creates unlimited training data from a single TTS source, eliminating the time-consuming process of manual speech collection and annotation while maintaining high transcription precision.
Data Source
AI summary
An exemplary computer system configured to train an ASR using the output from a TTS engine.