Conditional Factorization for Code-Switched and Monolingual ASR
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems struggle to effectively recognize monolingual and intra-sentential code-switched bilingual speech, often requiring resource-intensive methods that are specific to certain language pairs and fail to generalize well.
Innovation Solution
A conditional factorization method using a joint neural network model to decompose bilingual ASR into separate encoders for each language, followed by a joint network for fusion and decoding, allowing for efficient recognition of monolingual and intra-sentential code-switched utterances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ASR systems are designed to recognize intra-sentential code-switched bilingual speech, then bilingual speech recognition capability is improved, but system complexity and resource requirements increase
Solution Approach 1:
The system segments the bilingual speech recognition task into separate monolingual ASR systems for each language, where each encoder processes audio independently for its corresponding language. This segmentation allows the system to handle code-switched speech by combining results from multiple specialized subsystems rather than requiring a single complex bilingual model.
Solution Approach 2:
The proposed ASR system achieves multi-functionality by simultaneously handling monolingual speech recognition for multiple languages and intra-sentential code-switched speech recognition through a unified framework. The system can adapt to different language pairs without requiring language-specific customization, making it universally applicable across different bilingual scenarios.
2Measurement precision
If ASR systems are optimized for specific language pairs through explicit language identification, then code-switched recognition accuracy for those pairs is improved, but the system fails to generalize to other language pairs
Solution Approach 1:
The system achieves universal applicability across different language pairs by designing language-agnostic encoders that process audio inputs without requiring language-specific parameters. The framework can be applied to any bilingual scenario by simply providing audio data and corresponding transcripts for the target language pair, eliminating the need for explicit language identification or pair-specific optimization.
Solution Approach 2:
The system adapts to different language pairs by changing training parameters rather than structural parameters. By adjusting the training data and loss function weights for different language combinations, the same underlying model architecture achieves high accuracy across multiple language pairs without requiring structural modifications or explicit language identification mechanisms.
3Reliability
If resource-intensive methods are used to understand cross-lingual dynamics, then bilingual ASR performance is improved, but computational resources and time requirements increase
Solution Approach 1:
The system extracts and processes cross-lingual dynamics implicitly through the training data rather than explicitly modeling them. By taking out the need for separate cross-lingual analysis and embedding it within the end-to-end training process, the system achieves bilingual ASR performance without requiring resource-intensive cross-lingual dynamic modeling.
Solution Approach 2:
The ASR system performs self-service by automatically adapting to cross-lingual dynamics through the training process without requiring external linguistic analysis or manual configuration. The model learns language-specific and cross-lingual patterns directly from the training data, eliminating the need for separate resource-intensive cross-lingual dynamic understanding steps.
4Measurement precision
If separate monolingual ASR systems are used for each language, then monolingual recognition performance is maintained, but the system cannot handle intra-sentential code-switching
Solution Approach 1:
The system merges multiple separate monolingual ASR systems into a unified bilingual ASR framework that can handle code-switched speech. By combining the encoders and using a shared training objective that incorporates both monolingual and code-switched data, the system maintains strong monolingual performance while gaining the ability to recognize intra-sentential code-switching through the joint training process.
Data Source
AI summary
A method, apparatus, and non-transitory computer-readable medium for automatic speech recognition using conditional factorization for bilingual code-switched and monolingual speech may include receiving an audio observation sequence comprising a plurality of frames, the audio observation sequence including audio in a first language or a second language. The approach may further include mapping the audio observation sequence into a first sequence of hidden representations, the mapping being generated by a first encoder corresponding to the first language and mapping the audio observation sequence into a second sequence of hidden representations, the mapping being generated by a second encoder corresponding to the second language. The approach may further include generating a label-to-frame sequence based on the first sequence of hidden representations and the second sequence of hidden representations, using a joint neural network based model.