DEEP LEARNING MODEL FOR SPEECH-TO-TEXT CONVERSION

Abstract

The present invention provides a deep learning-based system for converting speech to text, utilizing a transformer-based neural network architecture to achieve high transcription accuracy across diverse languages, accents, and noisy environments. The system comprises an audio pre-processing module that enhances input audio quality by reducing noise and segmenting audio into feature-rich representations. A transformer model with self-attention and cross-attention layers captures phonetic, syntactic, and semantic context, ensuring continuity and precision in transcription. A post-processing module corrects grammatical errors and refines output text for readability. An adaptive learning module enables continuous improvement by updating the model based on user interactions, accommodating accent variations, domain-specific vocabulary, and new linguistic patterns. The system is optimized for real-time applications and can be deployed on resource-constrained devices, providing an efficient, versatile, and adaptable

Specification

Description:The embodiments of the present invention generally relates to the field of automatic speech recognition (ASR), particularly to a deep learning-based method and system for converting spoken language into text. This invention employs advanced neural network architectures to accurately transcribe speech in real-time, designed to handle complex language processing challenges such as accent variations, background noise, and domain-specific vocabulary, thus enhancing the effectiveness of speech-to-text applications across a range of industries.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Converting spoken , Claims:1. A system for converting speech to text, comprising:
an audio pre-processing module configured to receive and process audio signals by reducing noise, enhancing speech quality, and converting the audio into feature-rich representations;
a transformer-based neural network configured to encode the processed audio into intermediate feature representations using multiple attention layers to capture phonetic, syntactic, and semantic context;
a post-processing module configured to refine and correct the initial text transcription by applying linguistic rules and contextual error correction; and
an adaptive learning module that continuously updates the transformer model to recognize accents, dialects, and domain-specific vocabulary based on user interactions and feedback.

2. The system of claim 1, wherein the audio pre-processing module further includes a spectral analysis unit that converts the audio input into spectrograms to enhance the feature representation for the neural network.

3. The system of claim 1,

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