Multilingual Speech Recognition Through Transliteration and Transfer Learning
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Solution Overview
Problem
Conventional speech-to-text conversion methods require training multiple acoustic models and language models from scratch, which is time-consuming and expensive, especially for converting speech in multiple languages.
Innovation Solution
A system and method using machine transliteration and transfer learning to convert speech in any input language into text, utilizing a pre-trained English acoustic model to transliterate input into Latin or English characters, followed by reverse transliteration to the original language, reducing training time and computational cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple acoustic models and language models are trained from scratch for each language, then speech recognition accuracy for multiple languages is improved, but training time and computational cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training a single acoustic model on English data before using it for speech recognition in multiple languages through transliteration. This pre-trained model serves as a foundation that can be adapted to different languages without training from scratch, thereby reducing training time while maintaining recognition accuracy
Solution Approach 2:
The patent implements universality by designing a single acoustic model that can handle multiple languages through transliteration to English. Instead of creating separate models for each language, the same model is reused across different languages by converting non-English scripts to English equivalents, making the system multi-functional and reducing overall training requirements
2Reliability
If multiple acoustic models and language models are trained from scratch for each language, then speech recognition accuracy for multiple languages is improved, but computational cost increases significantly
Solution Approach 1:
The patent implements universality by designing a single acoustic model that can handle multiple languages through transliteration to English. Instead of creating separate models for each language, the same model is reused across different languages by converting non-English scripts to English equivalents, making the system multi-functional and reducing overall training requirements
Solution Approach 2:
The patent applies copying by using English text as an intermediate representation for multiple languages. Non-English speech is transcribed to English text through transliteration, and the same acoustic model processes all languages by treating them as English, effectively copying the English processing pipeline for other languages without requiring separate model training
3Measurement precision
If complex deep neural network layers are used in RNN models for speech transcription, then transcription accuracy is improved, but training complexity and implementation cost increase
Solution Approach 1:
The patent introduces English text as an intermediary between non-English speech input and the acoustic model. By transliterating non-English scripts to English equivalents, the system creates a common intermediate representation that simplifies the processing pipeline and reduces model complexity while maintaining transcription accuracy across multiple languages
Data Source
AI summary
A method for converting speech in one of a plurality of input languages into text using machine transliteration and transfer learning is disclosed. The method includes a training stage. The training stage includes receiving a training set of a plurality of audio files and an input text corresponding to the audio input in any input language using the speech recognition engine; transliterating the training set to transform the input text into transliterated text that includes characters of a base language and training acoustic model with the plurality of audio files and corresponding transliterated text using transfer learning. The method further includes an inference stage. The inference stage includes performing decoding on output of the trained acoustic model to generate text includes characters of the base language at inference and transliterating the generated text to output text includes characters in input language using reverse transliteration.