Speaker-Adaptive Speech Recognition Using DNN Bottleneck Layer
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Solution Overview
Problem
Conventional automatic speech recognition (ASR) systems face poor performance when recognizing speech from unknown speakers due to high speaker variability, especially when limited training data is available, as they require extensive data from multiple speakers for effective adaptation.
Innovation Solution
The development of a test-speaker-specific adaptive system using a bottleneck layer within a deep neural network (DNN) that adapts to individual speakers with minimal training data, integrating feature transformation and parameter tuning approaches to normalize speaker variability, allowing for efficient recognition with reduced data requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional ASR systems use training data from many speakers to handle speaker variability, then the system can accommodate different speakers, but the performance for unknown test speakers becomes poor and requires extensive training data
Solution Approach 1:
The patent segments the speaker adaptation process into two distinct stages: a generic model training stage using data from multiple speakers to capture common speech patterns, and a speaker-specific adaptation stage using a bottleneck layer to capture individual speaker characteristics. This segmentation allows the system to achieve both generalizability and speaker-specific accuracy without requiring extensive data from each individual speaker.
Solution Approach 2:
The bottleneck layer serves as an intermediary component between the generic DNN model and the final recognition output. It processes speaker-specific features in a compressed representation space, enabling the system to adapt to individual speakers efficiently while maintaining connection to the robust generic model. This intermediary structure resolves the contradiction by providing speaker-specific adaptation with minimal data requirements.
2Adaptability or versatility
If the system transforms input features before training the DNN to normalize speaker variability, then speaker adaptation is achieved, but the DNN must be re-trained for each new feature transformation
Solution Approach 1:
The patent implements a dynamic adaptation mechanism where the bottleneck layer parameters are updated using only the test speaker's data without requiring re-training of the entire DNN. This dynamic update approach allows the system to adapt to new speakers efficiently, resolving the contradiction between achieving speaker variability normalization and maintaining training efficiency.
3Measurement precision
If the system tunes network parameters using test speaker specific data to adapt to individual speakers, then speaker-specific performance improves, but more adaptive parameters are required and data requirements increase
Solution Approach 1:
The patent extracts and isolates speaker-specific information into a separate bottleneck layer, separating it from the main DNN architecture. This extraction allows the system to maintain a simple, fixed DNN structure while capturing speaker-specific variations through the bottleneck layer, thereby improving recognition accuracy without increasing overall system complexity or data requirements.
4Reliability
If extensive training data from the test speaker is used, then speaker adaptation performance improves, but the time required for adaptation increases
Solution Approach 1:
The patent applies partial action by using only a small portion of the test speaker's data (as little as one minute) to train the bottleneck layer, rather than requiring extensive training data. This partial data approach is sufficient to achieve reliable speaker adaptation, thereby reducing adaptation time while maintaining high recognition performance.
Data Source
AI summary
A method for generating a test-speaker-specific adaptive system for recognising sounds in speech spoken by a test speaker; the method employing:(i) training data comprising speech items spoken by the test speaker; and(ii) an input network component and a speaker adaptive output network, the input network component and speaker adaptive output network having been trained using training data from training speakers;the method comprising:(a) using the training data to train a test-speaker-specific adaptive model component of an adaptive model comprising the input network component, and the test-speaker-specific adaptive model component, and(b) providing the test-speaker-specific adaptive system comprising the input network component, the trained test-speaker-specific adaptive model component, and the speaker-adaptive output network.