Flexible Text Language Identification via Alphabet and N-gram Scoring
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Solution Overview
Problem
Current speech processing technologies face inaccuracies and inflexibility in text-to-speech conversions due to the need for large amounts of text data, especially when dealing with diverse languages, leading to unnatural speech output.
Innovation Solution
A method for flexible text-based language identification that analyzes short entries, such as single words, by calculating alphabet scores and n-gram frequency scores to determine the associated language, enabling accurate language modeling without requiring extensive data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of text data are used for language identification, then language modeling accuracy is improved, but system flexibility and adaptability deteriorate
Solution Approach 1:
The patent segments the language identification task into multiple independent scoring components: alphabet score calculation, n-gram frequency score calculation, and language determination. This segmentation allows the system to process short text entries efficiently by breaking down the analysis into manageable parts, thereby achieving accurate language identification without requiring large amounts of text data, thus maintaining system flexibility.
Solution Approach 2:
The patent changes the parameters used for language identification from traditional methods that require extensive text data to a hybrid approach using alphabet scores and n-gram frequency scores. By adjusting these parameters, the system can accurately identify languages from short text entries, resolving the contradiction between identification accuracy and system flexibility.
2Speed
If traditional phoneme to synthetic speech conversion is used, then processing speed is maintained, but speech naturalness deteriorates
Solution Approach 1:
The patent applies preliminary language identification using alphabet scores and n-gram frequency scores before performing text-to-speech conversion. This preliminary action determines the appropriate language model to use, ensuring that the subsequent phoneme to synthetic speech conversion uses the correct linguistic parameters, thereby maintaining both processing speed and speech naturalness.
3Adaptability or versatility
If minimal text data is used for language identification, then system flexibility is improved, but identification accuracy deteriorates
Solution Approach 1:
The patent creates a composite scoring system that combines multiple evaluation methods: alphabet score (assessing character compatibility with language alphabets) and n-gram frequency score (assessing word pattern likelihood). This composite approach allows the system to achieve high identification accuracy even with minimal text data by synthesizing information from multiple sources, thus maintaining both flexibility and accuracy.
Data Source
AI summary
An apparatus for providing flexible text based language identification includes an alphabet scoring element, an n-gram frequency element and a processing element. The alphabet scoring element may be configured to receive an entry in a computer readable text format and to calculate an alphabet score of the entry for each of a plurality of languages. The n-gram frequency element may be configured to calculate an n-gram frequency score of the entry for each of the plurality of languages. The processing element may be in communication with the n-gram frequency element and the alphabet scoring element. The processing element may also be configured to determine a language associated with the entry based on a combination of the alphabet score and the n-gram frequency score.