Neural Network Speaker Adaptation for Speech Recognition
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Solution Overview
Problem
On-line speaker adaptation technologies for speech recognition face performance degradation due to the mismatch between speech data used for i-vector extraction and recognition, as the i-vector extraction process is complex and requires a certain time length of speech data, leading to suboptimal recognition performance.
Innovation Solution
A method that uses a first neural network to extract a vector containing speaker recognition features from speech data, which is then used to compensate bias in a second neural network, transforming it from a speaker-independent to a speaker-dependent acoustic model for improved recognition performance, allowing for real-time adaptation with short-term data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If i-vector extraction is performed using traditional methods (Gaussian mixture model with T matrix multiplication), then speaker recognition features can be obtained, but the extraction process becomes complicated and requires a certain time length of speech data, causing mismatch between extraction data and recognition data
Solution Approach 1:
The patent replaces the traditional Gaussian mixture model-based i-vector extraction mechanism with a neural network-based speaker recognition feature extraction mechanism. This substitution simplifies the extraction process while maintaining or improving accuracy, and enables extraction from shorter speech segments that match the recognition data better.
Solution Approach 2:
The patent changes the fundamental parameters of the extraction system by using neural networks with learned parameters instead of fixed statistical models. This allows the system to adapt to different speech conditions and extract features from variable-length inputs, resolving the constraint of requiring fixed time-length speech data.
2Measurement precision
If i-vector extraction requires a certain time length of speech data, then more accurate speaker features can be extracted, but the extracted features do not match the speech data to be recognized, affecting recognition performance
Solution Approach 1:
The patent introduces dynamic adaptability by using neural networks that can process variable-length input sequences. The system dynamically adjusts the extraction process based on the available speech data length, ensuring that the extracted features always correspond to the actual recognition data regardless of its length.
Solution Approach 2:
The patent performs speaker feature extraction preliminary on the exact same speech data that will be used for recognition, rather than extracting from separate training data. This ensures perfect matching between the feature representation and the recognition target, eliminating the mismatch problem.
3Reliability
If speaker adaptation is implemented using traditional methods, then recognition performance can be improved, but computation complexity increases
Solution Approach 1:
The patent replaces complex traditional speaker adaptation mechanisms with a unified neural network framework. The neural network learns speaker-specific features directly from the input speech during recognition, eliminating the need for separate complex adaptation modules while improving both performance and computational efficiency.
Data Source
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AI summary
The present application discloses speech recognition methods and apparatuses. An exemplary method may include extracting, via a first neural network, a vector containing speaker recognition features from speech data. The method may also include compensating bias in a second neural network in accordance with the vector containing the speaker recognition features. The method may further include recognizing speech, via an acoustic model based on the second neural network, in the speech data.