Speech-Text Alignment for Robust Speech Language Models

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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-text alignments, using a modality adapter to extract speech features and a decoder to generate accurate transcriptions and responses.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional speech language models use standard speech recognition and natural language processing techniques, then the system can process speech audio efficiently, but transcription accuracy deteriorates when speech deviates from training data (unclear speech, accents, technical jargon, emotional tones, unusual pronunciations)

Engineering Contradiction:

Improvespeech processing efficiencyVSAvoidtranscription accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent introduces an intermediary alignment model that mediates between the speech recognition output and the language model input. This alignment model receives the transcribed text and audio features, aligns them based on temporal and semantic correspondence, and generates corrected text representations that preserve both transcription accuracy and contextual meaning, thereby resolving the contradiction between efficient processing and accurate transcription for atypical speech patterns

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent changes the parameter representation by introducing multiple attention mechanisms that dynamically adjust the weighting of different temporal segments and speech features. The model modifies the representation parameters by incorporating speaker characteristics, emotional tones, and contextual information, enabling accurate transcription and understanding even when speech deviates from standard patterns

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If the speech model is trained on diverse speech patterns to improve accuracy, then transcription quality improves, but the model complexity and training requirements increase

Engineering Contradiction:

Improvetranscription accuracyVSAvoidmodel training complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the speech processing task into distinct modules: a speech recognition component, an alignment model that processes both audio and text representations, and a language model component. This segmentation allows each module to be trained independently on specialized data, reducing overall training complexity while improving transcription accuracy for diverse speech patterns through focused learning in each segment

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent performs preliminary alignment of speech and text representations before the language model processes the input. By pre-aligning the audio features and transcribed text based on temporal correspondence and semantic meaning, the system prepares cleaned and contextualized text representations that simplify subsequent language modeling tasks, thereby reducing the complexity requirements for the language model component

Inventive Principle:

Principle #10Preliminary action

3Productivity

If the language model relies solely on transcribed text, then processing is simple and fast, but response relevance deteriorates when transcription is inaccurate

Engineering Contradiction:

Improveprocessing speedVSAvoidresponse relevance

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent merges the transcribed text representation with audio feature representations by introducing an alignment model that integrates both modalities. The model combines the linguistic information from text with the phonetic and contextual information from audio features, creating a unified representation that maintains processing efficiency while significantly improving response relevance for atypical speech patterns through multi-modal fusion

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS20260004772A1Techniques 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.