Text-to-Speech Snippet Fusion for Expressive Audio Output
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional text-to-speech systems lack the ability to incorporate audio snippets from media content items, such as music tracks, to enhance expressiveness and create more interesting or exciting output.
Innovation Solution
A text-to-speech system that includes a forced alignment data store and a combining engine to identify and combine audio snippets from multiple tracks based on forced alignment data, musical style similarities, and quality attributes, creating a more expressive and engaging audio output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional text-to-speech systems are used, then the system structure is simple, but the expressiveness and interest of the audio output are limited
Solution Approach 1:
The patent combines traditional speech synthesis with audio snippet extraction from media content items. The combining engine integrates synthesized speech segments with extracted audio snippets (music, sound effects, voice samples) to create enriched audio output. This merging of multiple audio sources directly addresses the contradiction by adding expressiveness and interest while maintaining a manageable system structure through modular integration.
Solution Approach 2:
The system segments the audio output into distinct components: synthesized speech portions and extracted audio snippet portions. The combining engine processes these segments separately and then integrates them. This segmentation allows the system to maintain simplicity in individual components while achieving complexity in the overall output, resolving the contradiction between expressiveness and system simplicity.
2Adaptability or versatility
If audio snippets from multiple tracks are incorporated, then the audio output becomes more interesting, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing media content items to extract and store audio snippets along with their metadata (duration, start time, end time, content description). This pre-extraction and indexing of audio snippets enables faster retrieval and integration during the main processing task, significantly reducing the processing time required when generating interesting audio output.
Solution Approach 2:
The system creates a database of pre-extracted audio snippets that can be quickly retrieved and reused. Instead of processing entire media files in real-time, the system copies and reuses pre-extracted audio snippet portions, reducing computational resources and processing time while maintaining the interest and expressiveness of the output.
3Measurement precision
If forced alignment data is used to precisely locate audio snippets, then the accuracy of text-to-speech conversion is improved, but the data processing complexity increases
Solution Approach 1:
The system introduces forced alignment data as an intermediary structure that bridges the text input and audio snippet extraction processes. This intermediary data format (containing text segments with corresponding time intervals and audio location information) simplifies the matching process between text and audio, providing precise location accuracy while avoiding complex real-time alignment computations during the main processing task.
Data Source
AI summary
A text-to-speech engine creates audio output that includes synthesized speech and one or more media content item snippets. The input text is obtained and partitioned into text sets. A track having lyrics that match a part of one of the text sets is identified. The location of the track's audio that contains the lyric is extracted based on forced alignment data. The extracted audio is combined with synthesized speech corresponding to the remainder of the input text to form audio output.