Language-Independent Neural Network for Multi-Lingual Speech Recognition
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Solution Overview
Problem
Conventional automatic speech recognition (ASR) systems require language-dependent resources, which are error-prone and increase development effort, complicating the development of multi-lingual recognition systems.
Innovation Solution
A language-independent neural network is constructed using multiple speech corpora to recognize spoken utterances and identify languages jointly, eliminating the need for language-dependent resources by using a hybrid attention/CTC architecture for end-to-end speech recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If language-dependent resources (pronunciation dictionaries, word segmentation) are used in conventional ASR systems, then speech recognition accuracy can be improved, but development effort and system complexity increase significantly
Solution Approach 1:
The patent extracts and removes language-dependent resources (pronunciation dictionaries, word segmentation models, phoneme representations) from the ASR system. By eliminating these intermediate representations and directly mapping acoustic features to character sequences, the system achieves language-independent operation while maintaining recognition accuracy through the attention mechanism and CTC loss function.
Solution Approach 2:
The patent creates a universal ASR system that can process multiple languages simultaneously using a single neural network architecture. The model uses a shared character set across languages and language identification tokens to handle different languages without requiring separate language-specific modules, achieving multi-functionality in a single system.
2Measurement precision
If language-dependent resources are hand-crafted for each language, then recognition performance can be optimized, but development time and effort greatly increase
Solution Approach 1:
The patent enables the system to automatically adapt to new languages without manual resource creation. The neural network learns language-specific patterns automatically from raw speech data and text transcripts through self-supervised learning, eliminating the need for hand-crafted pronunciation dictionaries and word segmentation rules for each language.
Solution Approach 2:
The patent performs preliminary training on multi-lingual data to pre-learning language-invariant acoustic features and character-level representations. This preliminary action allows the model to quickly adapt to new languages with minimal additional training, reducing development time while maintaining performance.
3Measurement precision
If separate language-dependent modules are used for each language, then language-specific accuracy can be improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent merges multiple language-specific processing paths into a single unified neural network. By combining language identification, acoustic feature processing, and character sequence generation into one end-to-end model, the system reduces resource requirements while maintaining language-specific accuracy through the attention mechanism that dynamically focuses on relevant features for each language.
Data Source
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AI summary
A method for training a multi-language speech recognition network includes providing utterance datasets corresponding to predetermined languages, inserting language identification (ID) labels into the utterance datasets, wherein each of the utterance datasets is labelled by each of the language ID labels, concatenating the labeled utterance datasets, generating initial network parameters from the utterance datasets, selecting the initial network parameters according to a predetermined sequence, and training, iteratively, an end-to-end network with a series of the selected initial network parameters and the concatenated labeled utterance datasets until a training result reaches a threshold.