Speech-Text Alignment for Accurate Responses to Diverse Speech

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Solution Overview

Problem

Conventional speech language models struggle with accurately interpreting speech audio that deviates from training data, leading to inaccurate transcriptions and irrelevant responses due to unclear speech, accents, technical jargon, emotional tones, or unusual pronunciations.

Innovation Solution

A two-stage training process for a speech language model that incorporates speech meta information and question-answer data to generate descriptive speech captions, bridging the modality gap between speech and text, and utilizing a modality adapter to extract relevant features.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional speech language models use standard speech recognition and natural language processing techniques, then the system structure is simple and easy to implement, but the model fails to accurately interpret speech audio that deviates from training data, leading to inaccurate transcriptions and irrelevant responses

Engineering Contradiction:

Improveaccuracy of speech interpretationVSAvoidcomplexity of model architecture

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces a visual language model as an intermediary component that processes visual representations of speech patterns. This visual language model acts as a mediator between the audio input and the traditional language model, enabling the system to interpret complex speech patterns that deviate from standard training data by first converting them into visual representations that can be understood and processed accurately.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If the speech model is trained on diverse speech patterns including accents and emotional tones, then the adaptability to various speech types improves, but the training data requirements and model complexity increase significantly

Engineering Contradiction:

Improveability to handle diverse speech patternsVSAvoidamount of training data required

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The patent replaces the traditional mechanical approach of training models on vast quantities of diverse audio data with a visual-based processing mechanism. By converting speech patterns into visual representations and using a visual language model to interpret these visuals, the system achieves high adaptability to diverse speech patterns without requiring proportionally large amounts of training data, as the visual representations capture the essential features of speech variations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20260004776A1Techniques for enhancing speech language models using descriptive speech-text alignment

Publication Date: 2026.01.01 NVIDIA CORP

AI summary

The disclosed method for generating a first depth map for responding to audio input includes processing the audio input using a trained encoder to generate a representation of the audio input, where the audio input includes speech; processing the representation of the audio input using a first trained adapter to generate one or more features; and processing the one or more features and text associated with the audio input using a trained language model to generate a response.