Text-to-Speech Synthesis Using Stored Speech Units
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Solution Overview
Problem
Current speech recognition systems lack the ability to generate text-to-speech output in a user's voice using previously captured speech, limiting personalized and natural human-computer interaction.
Innovation Solution
A system that captures and processes a user's speech to create a library of speech units, allowing for the generation of text-to-speech output by matching text messages with stored speech units and synthesizing audio in the user's voice, using techniques like Hidden Markov Models and unit selection for high-quality speech synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional speech recognition systems are used, then speech-to-text conversion is achieved, but text-to-speech generation in user's voice is not possible
Solution Approach 1:
The system performs preliminary speech capture and processing to build a library of speech units before actual text-to-speech generation is needed. This advance preparation enables the system to have user-specific voice data ready for future conversions, resolving the contradiction by establishing the capability beforehand rather than requiring it during operation.
Solution Approach 2:
The system creates copies of the user's speech data in the form of speech units that can be stored and reused. By capturing and storing multiple instances of speech units representing different phonetic elements, the system can reconstruct text-to-speech output in the user's voice without needing the original speech input, thus preserving voice characteristics while enabling versatile generation.
2Adaptability or versatility
If speech units are captured and stored for TTS generation, then personalized speech output is achieved, but system complexity increases
Solution Approach 1:
The system segments speech into discrete speech units representing phonetic elements rather than storing complete sentences or paragraphs. This segmentation allows the complex task of text-to-speech generation to be broken down into manageable components that can be stored, retrieved, and combined efficiently, reducing overall system complexity while maintaining personalization capability.
Solution Approach 2:
The system changes the parameters of speech representation by converting continuous speech signals into discrete speech units with specific phonetic characteristics. This parameter transformation enables the system to work with standardized, manageable data structures rather than complex continuous signals, simplifying the processing architecture while preserving the essential voice characteristics needed for personalized output.
3Measurement precision
If complete speech capture is performed for all text messages, then accurate TTS generation is achieved, but processing time increases
Solution Approach 1:
The system performs partial speech capture by focusing on capturing speech units for commonly used text messages rather than attempting to capture and process every possible message. This selective approach achieves sufficient accuracy for the most frequent communications while significantly reducing the overall processing time and resources required.
Solution Approach 2:
The system performs preliminary organization of speech units during the capture phase, structuring them in a way that enables efficient retrieval and matching during text-to-speech generation. This advance organization reduces the processing time required during actual message conversion, as the speech units are already prepared and indexed for quick access rather than requiring comprehensive real-time analysis.
Data Source
AI summary
Systems, methods, and devices for generating text-to-speech output using previously captured speech are described. Spoken audio is obtained and undergoes speech processing to create text. The resulting text is stored with the spoken audio, with both the text and the spoken audio being associated with the individual that spoke the audio. Various spoken audio and corresponding text are stored over time to create a library of speech units. When the individual sends a text message to a recipient, the text message is processed to determine portions of text, and the portions of text are compared to the library of text associated with the individual. When text in the library is identified, the system selects the spoken audio units associated with the identified stored text. The selected spoken audio units are then used to generate output audio data corresponding to the original text message, with the output audio data being sent to a device of the message recipient.