Big data drives the optimization of intelligent error-correction models for German speech synthesis technology

Abstract

In view of the problems such as pronunciation deviations, wrong stress placement, and abnormal prosody that occur frequently in German speech synthesis in practical applications, this paper builds a large-scale data-driven intelligent error correction model based on its own constructed large-scale data. Firstly, it integrates offline corpora and online logs, collecting approximately 2.1 million pieces of text and 1,300 hours of speech data. The offline corpora and user logs account for 61.5% and 38.5% of the total duration respectively. Through forced alignment and ASR playback, phoneme/word-level fine-grained error annotations are achieved, and text–phoneme–acoustic multimodal features and PCA reduction representations are constructed. On this basis, a "error detection sub-network + error correction generation sub-network" is attached to the acoustic layer of the German TTS baseline system. A joint loss combining weighted binary cross-entropy and L1/L2 combined regression is adopted for multitask end-to-end training on large-scale samples, and lightweight deployment is achieved through channel pruning, distillation, and quantization. Experimental results show that compared with the baseline system, the proposed method improves the error detection F1 score to 78.4% on the test set, reduces the MCD from 4.12dB to 3.76dB, increases the MOS from 3.85 to 4.21, and increases the RTF only from 0.78 to 0.83. This indicates that the intelligent error correction model significantly improves the objective quality and subjective listening experience of German speech synthesis without sacrificing real-time performance.