Acoustic Model Adaptation via Environmental Simulation
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Solution Overview
Problem
Conventional speech recognition systems face challenges in maintaining recognition accuracy due to differences in noise and reverberation between training and actual environments, requiring large amounts of adaptation data and potentially failing when environmental information discrepancies occur.
Innovation Solution
A speech recognition apparatus that generates an adapted acoustic model by simulating environmental conditions using sensor information, such as acoustic data, to mimic the actual environment, allowing for effective speech recognition even with limited target speech data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a base acoustic model trained on generic speech data is used, then speech recognition can be performed without environment-specific data, but speech recognition accuracy decreases due to differences in noise and reverberation between training and actual environments
Solution Approach 1:
The system performs preliminary acoustic model adaptation by generating pseudo target speech data that simulates the actual speech collection environment before performing speech recognition. This preliminary action involves creating adapted acoustic models that account for environment-specific characteristics like noise and reverberation, thereby improving recognition accuracy without requiring large amounts of actual target speech data.
Solution Approach 2:
The system generates pseudo target speech data by copying and transforming generic speech data to simulate the characteristics of the actual speech collection environment. This involves creating synthetic speech samples with matching noise and reverberation properties, which are then used to train adapted acoustic models that accurately represent the target environment.
2Measurement precision
If acoustic model adaptation is performed using speech data recorded in the target speech collection environment, then speech recognition accuracy is maintained, but a large amount of speech data must be prepared which incurs time cost
Solution Approach 1:
Instead of collecting and preparing large amounts of actual target speech data, the system copies generic speech data and transforms it into pseudo target speech data that simulates the target environment's acoustic characteristics. This copying approach dramatically reduces data preparation time while maintaining the benefits of environment-specific adaptation.
Solution Approach 2:
The system replaces the manual process of collecting and preparing target speech data with an automated computational process that generates pseudo target speech data through signal processing and simulation. This substitution eliminates the time-consuming data collection and preparation steps while achieving the same adaptation goal.
3Adaptability or versatility
If pseudo target speech data is generated by randomly setting environmental information, then adaptation can be performed without actual target data, but speech recognition accuracy decreases when there is a discrepancy between set environmental information and actual speech collection environment
Solution Approach 1:
The system uses feedback from the actual speech collection environment to guide the generation of pseudo target speech data. By analyzing the acoustic characteristics of the target environment and using this information to adjust the generation process, the system ensures that the pseudo data accurately reflects the actual environment, thereby maintaining high recognition accuracy.
Solution Approach 2:
The system dynamically adjusts environmental parameters such as noise characteristics and reverberation properties when generating pseudo target speech data. By changing these parameters to match the actual speech collection environment, the system ensures accurate adaptation without requiring random setting of environmental information.
Data Source
AI summary
According to one embodiment, a speech recognition apparatus includes processing circuitry. The processing circuitry generates, based on sensor information, environmental information relating to an environment in which the sensor information has been acquired, generates, based on the environmental information and generic speech data, an adapted acoustic model obtained by adapting a base acoustic model to the environment, acquires speech uttered in the environment as input speech data, and subjects the input speech data to a speech recognition process using the adapted acoustic model.