Amazon Technologies, Inc. (20240296827). TEXT-TO-SPEECH (TTS) PROCESSING simplified abstract

TEXT-TO-SPEECH (TTS) PROCESSING

Organization Name

Amazon Technologies, Inc.

Inventor(s)

Jaime Lorenzo Trueba of Cambridge (GB)

Thomas Renaud Drugman of Carnieres (BE)

Viacheslav Klimkov of Gdansk (PL)

Srikanth Ronanki of Cambridge (GB)

Thomas Edward Merritt of Cambridge (GB)

Andrew Paul Breen of Norwich (GB)

Roberto Barra-chicote of Cambridge (GB)

TEXT-TO-SPEECH (TTS) PROCESSING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240296827 titled 'TEXT-TO-SPEECH (TTS) PROCESSING

Simplified Explanation: This patent application describes a process where a speech model generates audio data from text input, with the help of a spectrogram estimator that analyzes the speech's frequency spectrogram and encodes various acoustic features into context vectors.

Key Features and Innovation:

• Speech model generates audio data from text input.
• Spectrogram estimator estimates frequency spectrogram of speech.
• Acoustic features like phonemes, syllable-level features, and word-level features are encoded into context vectors.
• Context vectors are used to condition the speech model.

Potential Applications: This technology can be used in various applications such as speech synthesis, language translation, voice assistants, and audio book narration.

Problems Solved: This technology addresses the challenge of accurately converting text into natural-sounding speech by incorporating detailed acoustic features and context vectors.

Benefits:

• Improved accuracy in text-to-speech processing.
• Enhanced naturalness and clarity in generated speech.
• Better performance in speech synthesis applications.

Commercial Applications: The technology can be utilized in industries such as telecommunications, entertainment, education, and accessibility services to enhance user experiences and communication efficiency.

Prior Art: Researchers can explore prior art related to speech synthesis, spectrogram analysis, and acoustic feature encoding to understand the evolution of this technology.

Frequently Updated Research: Stay updated on advancements in speech recognition, natural language processing, and machine learning techniques to enhance the capabilities of this technology.

Questions about Speech Synthesis: 1. How does this technology improve the accuracy of speech synthesis? 2. What are the potential limitations of using context vectors in conditioning the speech model?

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Original Abstract Submitted

during text-to-speech processing, a speech model creates output audio data, including speech, that corresponds to input text data that includes a representation of the speech. a spectrogram estimator estimates a frequency spectrogram of the speech; the corresponding frequency-spectrogram data is used to condition the speech model. a plurality of acoustic features corresponding to different segments of the input text data, such as phonemes, syllable-level features, and/or word-level features, may be separately encoded into context vectors; the spectrogram estimator uses these separate context vectors to create the frequency spectrogram.