Real-Time, High-Accuracy Speech-to-Speech Translation System

Abstract

This invention introduces a groundbreaking real-time speech-to-speech translation system that significantly advances the state-of-the-art in language translation. The system leverages advanced deep learning techniques to seamlessly translate spoken language between multiple languages, overcoming the limitations of traditional translation methods. By integrating speech recognition, machine translation, and text-to-speech synthesis into a unified end-to-end architecture, the system achieves remarkable accuracy and low latency. The speech recognition module accurately transcribes spoken language into text, even in noisy environments. The machine translation module, powered by state-of-the-art neural machine translation models, generates fluent and accurate translations. Finally, the text-to-speech synthesis module produces natural-sounding speech in the target language, providing a seamless user experience. This innovative system has the potential to revolutionize language communication and facilitate global understanding. It can be applied in various domains, including international business, tourism, education, and healthcare, breaking down language barriers and fostering cross-cultural exchange.

Specification

Description:Field of the Invention
[001] This invention pertains to the field of artificial intelligence, natural language processing, and speech recognition, specifically a system and method for real-time speech-to-speech translation between multiple languages with high accuracy.
Background of the Invention
[002] Traditional methods of language translation have relied on human translators, which can be a time-consuming and costly process. Moreover, human translation can be prone to errors, particularly in complex or nuanced language. Machine translation systems have emerged as an alternative to human translation, but they often suffer from latency issues, low accuracy, and limited language support.
[003] Recent advancements in deep learning, particularly neural machine translation (NMT) models, have significantly improved the quality of machine translation. However, real-time speech-to-speech translation remains a challenging task. It requires accurate speech recognition, efficient translation, and fast text-to-speech synthesis to ensure a seamless user experience.
[004] Existing speech-to-speech translation systems often rely on a pipeline approach, involving separate modules for speech recognition, machine translation, and text-to-speech synthesis. This pipeline approach can introduce latency and degrade the overall system performance, especially in real-time applications.
Summary of the Invention
[005] This invention presents a novel real-time speech-to-speech translation system that leverages advanced deep learning techniques to achieve high accuracy and low latency. The system comprises a speech recognition module, a neural machine translation module, and a text-to-speech synthesis module, all integrated into a unified end-to-end architecture.
[006] The speech recognition module accurately transcribes spoken language into text, even in noisy environments. The neural machine translation module translates the transcribed text into the target language, leveraging a powerful sequence-to-sequence model. The text-to-speech synthesis module generates natural-sounding speech in the target language, providing a seamless user experience.
Detailed Description
[007] The proposed system employs a deep neural network architecture that integrates speech recognition, machine translation, and text-to-speech synthesis into a single end-to-end model. This integrated approach enables efficient and accurate real-time translation.
Speech Recognition Module
[008] The speech recognition module utilizes state-of-the-art techniques to accurately transcribe spoken language into text. Key components and techniques include:
• Acoustic Modeling: Employs deep neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to model the acoustic properties of speech signals.
• Language Modeling: Utilizes statistical language models to predict the most likely sequence of words given the acoustic input.
• Decoding Algorithms: Leverages decoding algorithms, such as beam search and connectionist temporal classification (CTC), to generate the most probable transcription.
• Noise Reduction and Echo Cancellation: Incorporates techniques to mitigate the effects of noise and echo in the input audio signal.
Neural Machine Translation Module
[009] The neural machine translation module translates the transcribed text into the target language. Key components and techniques include:
• Encoder-Decoder Architecture: Employs an encoder-decoder architecture to process the source language and generate the target language.
• Attention Mechanism: Utilizes attention mechanisms to focus on relevant parts of the source sequence during the translation process.
• Transformer Architecture: Leverages the transformer architecture, which is particularly effective for long-sequence tasks.
• Multilingual Training: Trains the model on large-scale multilingual datasets to improve translation quality for multiple language pairs.
Text-to-Speech Synthesis Module
[010] The text-to-speech synthesis module generates natural-sounding speech in the target language. Key components and techniques include:
• Neural Text-to-Speech (TTS) Models: Employs advanced neural TTS models, such as Tacotron2 and WaveNet, to synthesize high-quality speech.
• Voice Cloning: Allows for the creation of synthetic voices that closely resemble specific individuals.
• Emotional Speech Synthesis: Enables the generation of speech with various emotional expressions.
• Prosody Control: Provides control over the prosodic features of the synthesized speech, such as pitch, intonation, and rhythm.
, Claims:1. A system for real-time speech-to-speech translation, comprising:
o A speech recognition module configured to transcribe spoken language into text.
o A neural machine translation module configured to translate the transcribed text into a target language.
o A text-to-speech synthesis module configured to generate speech in the target language.
2. The system of claim 1, wherein the speech recognition module is a recurrent neural network or a transformer-based model.
3. The system of claim 1, wherein the neural machine translation module is a transformer-based model.
4. The system of claim 1, wherein the text-to-speech synthesis module is a neural text-to-speech model.
5. The system of claim 1, further comprising a language identification module configured to automatically detect the source language of the input speech.
6. The system of claim 1, wherein the neural machine translation module is configured to employ attention mechanisms to improve translation quality.
7. The system of claim 1, wherein the neural machine translation module is configured to utilize back-translation techniques to enhance model training.
8. The system of claim 1, wherein the text-to-speech synthesis module is configured to customize the synthesized voice to match specific speaker styles or accents.

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