Multimodal Language Model for Unified Speech and Text Tasks
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Solution Overview
Problem
Conventional systems require separate models for different tasks involving speech and text, leading to inefficiencies in training and inference, and often overlook important aspects like paralinguistic features and computational efficiency.
Innovation Solution
A multimodal architecture using a single language model neural network trained on both text and audio data, allowing it to perform tasks such as speech recognition, translation, and synthesis directly from audio inputs, preserving linguistic and paralinguistic information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate models are used for different speech and text tasks, then task-specific performance can be optimized, but system complexity and training resources increase
Solution Approach 1:
The patent implements a single language model that can perform multiple speech and text tasks including speech recognition, translation, and synthesis. The model processes audio inputs directly and generates text outputs, preserving both linguistic and paralinguistic information across different task types. This universal approach eliminates the need for separate specialized models while maintaining competitive performance across tasks.
2Reliability
If separate models are used for different tasks, then task optimization is possible, but training time and resources increase
Solution Approach 1:
The patent combines multiple task objectives into a single unified training process. The language model is trained on diverse datasets encompassing speech recognition, translation, and synthesis tasks simultaneously, allowing the model to learn shared representations and transfer knowledge across tasks. This unified training approach reduces total training time compared to training separate models independently while maintaining task-specific performance through multi-objective optimization.
3Ease of operation
If conventional separate models are used, then each task can be handled independently, but paralinguistic features are overlooked
Solution Approach 1:
The unified language model processes audio inputs directly without converting to text first, preserving paralinguistic features such as tone, emotion, and speaker characteristics throughout the processing pipeline. The model's architecture allows it to capture both linguistic content and paralinguistic cues simultaneously, then utilize this rich representation for various downstream tasks including speech recognition, translation, and synthesis, ensuring paralinguistic information is retained and leveraged across all tasks.
4Productivity
If a single multimodal model is used, then training efficiency improves, but computational requirements per task may increase
Solution Approach 1:
The patent merges multiple task processing into a single computational pass through the unified language model. By processing speech recognition, translation, and synthesis tasks through one model architecture with shared layers and parameters, the system avoids redundant computations that would occur with separate models. The model processes audio inputs once and generates appropriate outputs based on the task, reducing overall computational requirements despite the multimodal nature of the tasks.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing tasks. One of the methods includes obtaining a sequence of input tokens, where each token is selected from a vocabulary of tokens that includes text tokens and audio tokens, and wherein the sequence of input tokens includes tokens that describe a task to be performed and data for performing the task; generating a sequence of embeddings by embedding each token in the sequence of input tokens in an embedding space; and processing the sequence of embeddings using a language model neural network to generate a sequence of output tokens for the task, where each token is selected from the vocabulary.