PaddleSpeech is an open-source all-in-one speech toolkit. It aims at facilitating the development and research of speech processing technologies by providing an easy-to-use command-line interface and a simple code structure. This paper describes the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to-speech tasks. PaddleSpeech achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods. It also provides recipes and pretrained models to quickly reproduce the experimental results in this paper. PaddleSpeech is publicly avaiable at https://github.com/PaddlePaddle/PaddleSpeech.

This paper reports on the first participation of TCH (Toshiba (China) Research and Development Center) at the IWSLT evaluation campaign. We participated in all the 5 translation tasks with Chinese as source language or target language. For Chinese-English and English-Chinese translation, we used hybrid systems that combine rule-based machine translation (RBMT) method and statistical machine translation (SMT) method. For Chinese-Spanish translation, phrase-based SMT models were used. For the pivot task, we combined the translations generated by a pivot based statistical translation model and a statistical transfer translation model (firstly, translating from Chinese to English, and then from English to Spanish). Moreover, for better performance of MT, we improved each module in the MT systems as follows: adapting Chinese word segmentation to spoken language translation, selecting out-of-domain corpus to build language models, using bilingual dictionaries to correct word alignment results, handling NE translation and selecting translations from the outputs of multiple systems. According to the automatic evaluation results on the full test sets, we top in all the 5 tasks.