In-Vehicle Voice Assist Using Latent Audio for Idiom Recognition
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Solution Overview
Problem
Tokenization-based voice recognition systems face challenges with ambiguities due to homophone tokens, particularly in understanding language-specific expressions like idioms, poems, and slang, leading to errors in voice recognition.
Innovation Solution
A voice assist system using a variational autoencoder encodes audio data into a latent space, incorporating contextual data to generate expressions without lexical tokenization, allowing for accurate recognition of natural language commands in vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lexical tokenization is used for voice recognition, then the system can process standard language, but it introduces ambiguities with homophone tokens and fails to understand language-specific expressions like idioms, poems, and slang
Solution Approach 1:
The patent replaces the mechanical lexical tokenization system with a neural network-based acoustic model. Instead of breaking down speech into discrete word tokens that rely on predefined vocabularies, the system uses continuous acoustic feature extraction and neural network processing to directly map audio signals to semantic meanings, thereby eliminating homophone ambiguities and improving understanding of language-specific expressions.
Solution Approach 2:
The patent changes the fundamental parameters of voice recognition from discrete token-based representation to continuous acoustic feature representation. By transforming the input from segmented lexical tokens to continuous spectral features processed by neural networks, the system achieves both higher accuracy and better adaptability to various language expressions including idioms, poems, and slang.
2Measurement precision
If a variational autoencoder is used to encode audio data into latent space, then the system can generate accurate expressions without tokenization, but the device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the variational autoencoder on large datasets to learn effective latent space representations before deploying it for actual voice recognition tasks. The encoder-decoder architecture is pre-configured with learned weights and structures that enable it to automatically transform audio data into meaningful expressions without requiring complex post-processing or tokenization steps during operation.
Data Source
AI summary
A method for voice assistance includes receiving, by a vehicle controller of a vehicle, audio data. The audio data is indicative of a voice command uttered by a user of the vehicle in natural language. The method also includes encoding, using an encoder of a variational autoencoder, the audio data into a latent space to generate encoded data. The method also includes receiving contextual data relating to the voice command uttered by the user of the vehicle. The method also includes generating, using a decoder of the variational autoencoder, an expression from the encoded data and the contextual data. The expression is representative of the audio data. The method also includes commanding, using the vehicle controller, the vehicle to generate a response based on the expression generated by the decoder of the variational autoencoder.