AI Speech-to-Text Conversion for Mixed-Language Inputs
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Solution Overview
Problem
Traditional speech-to-text engines struggle with mixed-language inputs, such as Hinglish, leading to incorrect transcriptions due to limited language-specific training and failure to handle multiple languages or dialects effectively.
Innovation Solution
A system and method that utilizes an acoustic engine to extract attributes from speech inputs, predicting characters and dialects, combined with an AI engine to determine a corpus of sentences in a predefined language, generating accurate textual outputs for multi-lingual and dialectal inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If language specific speech to text engines are used, then training data requirements are reduced, but accuracy deteriorates when handling mixed-language inputs
Solution Approach 1:
The patent creates a universal speech-to-text engine that can handle multiple languages and mixed-language inputs (like Hinglish) simultaneously. Instead of separate language-specific models, a single model is trained on multi-lingual data to perform the function of transcribing speech in any language combination, thereby reducing the need for multiple separate training datasets while maintaining high accuracy across all language scenarios.
Solution Approach 2:
The patent uses composite training data that combines multiple languages and dialects into a unified training corpus. This composite dataset includes mixed-language speech samples, allowing the model to learn patterns from diverse linguistic inputs and produce accurate transcriptions for composite language scenarios that neither pure language-specific models could handle effectively.
2Device complexity
If language specific models are used, then model complexity is reduced, but adaptability deteriorates for mixed-language and dialectal inputs
Solution Approach 1:
The patent develops a universal speech-to-text model that serves multiple language and dialectal functions within a single system architecture. This multi-functional approach allows the model to adapt to various language combinations and dialects without requiring separate complex models for each language, thereby maintaining manageable complexity while significantly improving adaptability.
Solution Approach 2:
The patent implements a dynamic language identification and selection mechanism that adapts the model's behavior based on the detected language composition in the input speech. The system dynamically adjusts its processing to handle mixed-language inputs, switching between different language processing pathways as needed, which enhances adaptability without permanently increasing structural complexity.
3Speed
If traditional speech to text engines are used, then processing speed is improved, but reliability deteriorates for alien words in mixed-language speech
Solution Approach 1:
The patent employs composite training data that includes mixed-language speech samples and alien words from multiple languages. This composite corpus enables the model to recognize and accurately transcribe foreign words and code-switched expressions while maintaining processing speeds comparable to traditional engines, as the model learns to identify these patterns during training rather than requiring complex real-time analysis.
Data Source
AI summary
A system and a method for enabling automatic conversion of speech input to text is disclosed. The method involves receiving an audio file pertaining to speech of a user, extracting a first set of attributes indicative of plurality of time frames spaced along the duration of the audio file, extracting a second set of attributes indicative of speech patterns, predicting a first set of characters to generate a first output sentence, determining, through an artificial intelligence (AI) engine, a first data set comprising a corpus of sentences of a predefined language based on a predefined language usage parameters, and generating a textual output in the predefined language based on a combination of the extracted first and second set of attributes, the predicted first set of characters, and the AI engine based determination.