Text-to-Speech Toolkit for Phoneme Labeling Error Correction
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Solution Overview
Problem
The process of building a text-to-speech voice is complex and error-prone due to inconsistencies in phoneme labeling, requiring manual correction of thousands of audio and text files, and lacks integrated change management for collaborative work, leading to degraded voice quality and increased testing time.
Innovation Solution
A toolkit for generating a text-to-speech voice that includes methods for tracking progress, error detection, and pronunciation dictionary management, enabling multiple workers to efficiently process and correct phoneme databases, and reducing the time and cost of voice generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If Automatic Speech Recognition (ASR) is used to automatically label phonemes, then the labeling process becomes faster and more scalable, but the accuracy of phoneme labeling deteriorates due to inherent ASR error rates
Solution Approach 1:
The system implements a feedback mechanism where human reviewers verify and correct ASR-labeled phonemes. The toolkit tracks which utterances have been processed by ASR and identifies those requiring human review, creating a closed-loop system that continuously improves accuracy while maintaining automated processing for confident cases.
Solution Approach 2:
Instead of manually labeling all phonemes or relying solely on ASR, the system applies a partial approach: ASR processes all utterances initially, and only the uncertain or erroneous portions are subjected to human review. This selective application of human expertise optimizes the balance between speed and accuracy.
2Productivity
If multiple workers process audio and text files in parallel, then the voice building process becomes more efficient, but coordination and error management become more complex
Solution Approach 1:
The toolkit segments the voice building process into discrete, trackable tasks for individual utterances. Each worker can independently process specific utterances without interfering with others, as the system manages task assignment and state tracking at the utterance level rather than requiring complex coordination between workers.
Solution Approach 2:
The toolkit serves as an intermediary system that coordinates between multiple workers and the phoneme database. It manages task queues, tracks processing states, and handles error logging, thereby abstracting away the coordination complexity from individual workers while enabling parallel processing.
3Ease of manufacture
If traditional hit-or-miss testing methods are used to find synthesis errors, then the testing process is simpler to implement, but the time required to detect errors increases significantly
Solution Approach 1:
The system performs preliminary error detection by automatically analyzing and tracking potential errors in the phoneme database before final synthesis. The toolkit identifies and logs errors during the voice building process itself, so that when synthesis occurs, fewer errors remain undetected, reducing the need for extensive post-synthesis testing.
4Reliability
If a comprehensive pronunciation dictionary with all variant pronunciations is created, then ASR accuracy improves, but the dictionary size and processing complexity increase
Solution Approach 1:
The system applies local quality by maintaining detailed pronunciation variants only where needed for specific words or contexts, rather than uniformly expanding the entire dictionary. The toolkit allows selective addition of pronunciation variants based on actual usage patterns and error rates, optimizing the balance between coverage and size.
Data Source
AI summary
Disclosed herein are various aspects of a toolkit used for generating a TTS voice for use in a spoken dialog system. The embodiments in each case may be in the form of the system, a computer-readable medium or a method for generating the TTS voice. An embodiment of the invention relates to a method of tracking progress in developing a text-to-speech (TTS) voice. The method comprises insuring that a corpus of recorded speech contains reading errors and matches an associated written text, creating a tuple for each utterance in the corpus and tracking progress for each utterance utilizing the tuple. Various parameters may be tracked using the tuple but the tuple provides a means for enabling multiple workers to efficiently process a database of utterance in preparation of a TTS voice.