Accent Clustering for Fair Speech Recognition on Atypical Accents
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems perform poorly on atypical accented speech due to biased training data distribution, leading to poor recognition accuracy and inefficiencies in collecting and verifying accented speech data.
Innovation Solution
A two-stage training process for an accent recognition model using supervised distributionally robust optimization and unsupervised clustering to predict accent representations, followed by data mining to train an ASR model on clustered and mined accented speech utterances, enhancing recognition quality for under-represented accents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ASR systems are trained on large data sets with biased distribution toward common accents, then training efficiency is improved, but recognition accuracy on atypical accented speech deteriorates
Solution Approach 1:
The system performs preliminary accent clustering and identification on the training data before ASR model training. By pre-processing the data to identify and separate accented speech patterns, the system prepares balanced accent representations in advance, allowing the ASR model to learn from diverse accents without requiring excessive training data for each accent type.
Solution Approach 2:
The system applies different processing strategies to different accents based on their characteristics. By identifying specific accent patterns and applying accent-aware processing, the system tailors the training approach to handle under-represented accents differently from common accents, improving recognition accuracy for atypical speech patterns while maintaining overall training efficiency.
2Measurement precision
If manual collection and verification of accented speech data is performed, then data quality for under-represented accents is improved, but time consumption and costs increase
Solution Approach 1:
The system performs self-service accent identification and clustering automatically without requiring manual verification. The accent recognition model autonomously processes training data to identify accented speech patterns and generate balanced accent representations, eliminating the need for time-consuming manual data collection and verification while maintaining high data quality for under-represented accents.
Solution Approach 2:
The system replaces manual mechanical processes of data collection and verification with automated computational processes. The accent recognition model and clustering algorithms substitute human effort with machine-based automatic accent identification, significantly reducing time consumption while improving consistency and scalability of the data preparation process.
3Measurement precision
If accent recognition model uses complex architecture with multiple attention layers, then accent representation accuracy is improved, but computational complexity increases
Solution Approach 1:
The system extracts only the essential accent-related features from speech data using the accent recognition model, separating accent characteristics from other speech components. By focusing on extracting specific accent representations rather than processing entire speech signals through complex ASR pipelines, the system achieves accurate accent identification with optimized computational requirements.
Solution Approach 2:
The system segments the speech processing task into distinct components: accent recognition, accent clustering, and ASR training. By dividing the overall process into separate modules with specialized functions, the system reduces the complexity of each individual component while maintaining high overall accuracy through coordinated operation of multiple specialized subsystems.
Data Source
AI summary
A method of training an accent recognition model includes receiving a corpus of training utterances spoken across various accents, each training utterance in the corpus including training audio features characterizing the training utterance, and executing a training process to train the accent recognition model on the corpus of training utterances to teach the accent recognition model to learn how to predict accent representations from the training audio features. The accent recognition model includes one or more strided convolution layers, a stack of multi-headed attention layers, and a pooling layer configured to generate a corresponding accent representation.