Speaker Vector Integration in RNN Language Models
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Solution Overview
Problem
Conventional recurrent neural network-based language models do not account for speakers, making it impossible to explicitly use information about who has spoken and who will speak next in multi-person conversations, limiting their effectiveness in speech recognition and other applications.
Innovation Solution
A language model score calculation apparatus that incorporates speaker information by converting speaker labels into speaker vectors and combining them with word history vectors to calculate prediction probabilities, allowing the model to account for speaker-specific characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional recurrent neural network based language model is used, then language prediction performance is high, but speaker information cannot be explicitly utilized
Solution Approach 1:
The patent embeds speaker identity information within the existing word history vector structure. The speaker vector is integrated into the recurrent neural network's hidden state, allowing speaker information to be nested within the same computational framework that processes word sequences, thus preserving information without significantly increasing external complexity
Solution Approach 2:
The patent introduces a speaker vector as an intermediary representation that bridges speaker identity and the language model. This speaker vector acts as a mediator that encodes speaker-specific characteristics and is combined with word history vectors, enabling the model to utilize speaker information without directly modifying the core recurrent neural network architecture
2Measurement precision
If speaker information is incorporated into the language model, then word prediction sophistication is improved, but model complexity increases
Solution Approach 1:
The patent applies local quality by maintaining different representation types for different aspects of information. Word vectors capture lexical meaning while speaker vectors capture speaker-specific characteristics. Each type of vector maintains its own properties and is processed appropriately within the network, allowing the model to handle multiple types of information with specialized representations rather than forcing a uniform structure
Solution Approach 2:
The patent merges speaker vectors with word history vectors in the recurrent neural network to create a unified representation that incorporates both speaker identity and linguistic context. This combining is done through concatenation or addition operations that integrate the two types of information while maintaining computational efficiency, thus improving prediction accuracy without proportionally increasing complexity
Data Source
AI summary
A language model score calculation apparatus calculates a prediction probability of a word wi as a language model score of a language model based on a recurrent neural network. The language model score calculation apparatus includes a memory; and a processor configured to execute converting a word wi-1 that is observed immediately before the word wi into a word vector ϕ(wi-1); converting a speaker label ri-1 corresponding to the word wi-1 and a speaker label ri corresponding to the word wi into a speaker vector ψ(ri-1) and a speaker vector ψ(ri), respectively; calculating a word history vector si by using the word vector ϕ(wi-1), the speaker vector ψ(ri-1), and a word history vector si-1 that is obtained when a prediction probability of the word wi-1 is calculated; and calculating a prediction probability of the word wi by using the word history vector si-1 and the speaker vector ψ(ri).