Bibliographic Details
| Title: |
SpeeChain: A Speech Toolkit for Large-Scale Machine Speech Chain |
| Authors: |
Qi, Heli, Novitasari, Sashi, Tjandra, Andros, Sakti, Sakriani, Nakamura, Satoshi |
| Publication Year: |
2023 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Computation and Language, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing, 68T10, I.2.7 |
| More Details: |
This paper introduces SpeeChain, an open-source Pytorch-based toolkit designed to develop the machine speech chain for large-scale use. This first release focuses on the TTS-to-ASR chain, a core component of the machine speech chain, that refers to the TTS data augmentation by unspoken text for ASR. To build an efficient pipeline for the large-scale TTS-to-ASR chain, we implement easy-to-use multi-GPU batch-level model inference, multi-dataloader batch generation, and on-the-fly data selection techniques. In this paper, we first explain the overall procedure of the TTS-to-ASR chain and the difficulties of each step. Then, we present a detailed ablation study on different types of unlabeled data, data filtering thresholds, batch composition, and real-synthetic data ratios. Our experimental results on train_clean_460 of LibriSpeech demonstrate that our TTS-to-ASR chain can significantly improve WER in a semi-supervised setting.
Comment: Submitted to ICASSP 2023 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2301.02966 |
| Accession Number: |
edsarx.2301.02966 |
| Database: |
arXiv |
