Universal Phoneme Decoder for Spoken Language Identification
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Solution Overview
Problem
In continuous speech, it is challenging to predict the beginning and ending of words in a stream of speech, making it difficult to accurately identify spoken languages.
Innovation Solution
A language identification engine with a universal phoneme decoder and statistical language models that analyze phoneme sequences to distinguish between candidate languages, using a front-end module to filter and decode audio streams, and a run-time identifier to determine the most likely spoken language based on phoneme patterns and probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional speech recognition methods are used to identify words in continuous speech, then the system can process speech streams, but it cannot accurately predict word boundaries and endings in continuous speech
Solution Approach 1:
The patent segments continuous speech into discrete phoneme units using a universal phoneme decoder. By breaking down the continuous speech stream into individual phonemes and analyzing their sequence patterns, the system can identify language characteristics without needing to predict word boundaries explicitly. This segmentation approach transforms the complex continuous speech analysis into manageable discrete unit processing.
2Measurement precision
If a universal phoneme decoder with comprehensive phoneme sets is used to capture phoneme correspondences across multiple languages, then language identification accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent pre-computes and stores phoneme sequence patterns and their probabilities for multiple candidate languages during an offline training phase. The statistical language models are trained in advance with phoneme sequences from training audio streams, creating ready-to-use probability distributions. During runtime, the system only needs to query these pre-computed models rather than performing complex calculations on the fly, significantly reducing processing time while maintaining high accuracy.
3Adaptability or versatility
If statistical language models are trained with phoneme sequences from multiple candidate languages to improve language identification, then the ability to distinguish between languages is enhanced, but the model training complexity and data requirements increase
Solution Approach 1:
The patent employs a universal phoneme decoder that works across multiple languages simultaneously, rather than requiring separate decoders for each language. The statistical language models are designed to handle multiple candidate languages in a unified framework, where each model computes probabilities for different languages based on the same phoneme sequence input. This universal approach reduces overall system complexity compared to maintaining separate specialized systems for each language.
Data Source
AI summary
A language identification system that includes a universal phoneme decoder (UPD) is described. The UPD contains a universal phoneme set representing both 1) all phonemes occurring in the set of two or more spoken languages, and 2) captures phoneme correspondences across languages, such that a set of unique phoneme patterns and probabilities are calculated in order to identify a most likely phoneme occurring each time in the audio files in the set of two or more potential languages in which the UPD was trained on. Each statistical language model (SLM) uses the set of unique phoneme patterns created for each language in the set to distinguish between spoken human languages in the set of languages. The run-time language identifier module identifies a particular human language being spoken by utilizing the linguistic probabilities supplied by the SLMs that are based on the set of unique phoneme patterns created for each language.