The variety and complexity of accents pose a huge challenge to robust Automatic Speech Recognition (ASR). Some previous work has attempted to address such problems, however most of the current approaches either require prior knowledge about the target accent, or cannot handle unseen accents and accent-unspecific standard speech. In this work, we aim to improve multi-accent speech recognition in the end-to-end (E2E) framework with a novel layer-wise adaptation architecture. Firstly, we propose a robust deep accent representation learning architecture to obtain accurate accent embedding, and some advanced schemes are designed to further boost the quality of accent embeddings, including phone posteriorgram (PPG) feature, TTS based data augmentation in the training stage, test-time augmentation and multi-embedding fusion in the testing stage. Then, the layer-wise adaptation with accent embeddings is developed for fast accent adaptation in ASR, and two types of adapter layers are designed, including the gated adapter layer and multi-basis adapter layer. Compared to the usual two-pass adaptation, these adapter layers are injected between the ASR encoder layers to encode the accent information in ASR flexibly, and perform fast adaption on the corresponding speech accent. The experiments on Accent AESRC corpus show that the proposed deep accent representation learning can capture accurate accent knowledge, and get high performance on accent classification. The new layer-wise adaptation architecture with the accurate accent embedding outperforms the other traditional methods, and obtains consistent$\sim$15% relative word error rate (WER) reduction on all kinds of testing scenarios, including seen accents, unseen accents and accent-unspecific standard speech.

This paper describes the AISpeech-SJTU system for the accent identification track of the Interspeech-2020 Accented English Speech Recognition Challenge. In this challenge track, only 160-hour accented English data collected from 8 countries and the auxiliary Librispeech dataset are provided for training. To build an accurate and robust accent identification system, we explore the whole system pipeline in detail. First, we introduce the ASR based phone posteriorgram (PPG) feature to accent identification and verify its efficacy. Then, a novel TTS based approach is carefully designed to augment the very limited accent training data for the first time. Finally, we propose the test time augmentation and embedding fusion schemes to further improve the system performance. Our final system is ranked first in the challenge and outperforms all the other participants by a large margin. The submitted system achieves 83.63% average accuracy on the challenge evaluation data, ahead of the others by more than 10% in absolute terms.

Data augmentation is commonly used to help build a robust speaker verification system, especially in limited-resource case. However, conventional data augmentation methods usually focus on the diversity of acoustic environment, leaving the lexicon variation neglected. For text dependent speaker verification tasks, it’s well-known that preparing training data with the target transcript is the most effectual approach to build a well-performing system, however collecting such data is time-consuming and expensive. In this work, we propose a unit selection synthesis based data augmentation method to leverage the abundant text-independent data resources. In this approach text-independent speeches of each speaker are firstly broke up to speech segments each contains one phone unit. Then segments that contain phonetics in the target transcript are selected to produce a speech with the target transcript by concatenating them in turn. Experiments are carried out on the AISHELL Speaker Verification Challenge 2019 database, the results and analysis shows that our proposed method can boost the system performance significantly.

In recent years, finger vein recognition has become an important sub-field in biometrics and been applied to real-world applications. The development of finger vein recognition algorithms heavily depends on large-scale real-world data sets. In order to motivate research on finger vein recognition, we released the largest finger vein data set up to now and hold finger vein recognition competitions based on our data set every year. In 2017, International Competition on Finger Vein Recognition (ICFVR) is held jointly with IJCB 2017. 11 teams registered and 10 of them joined the final evaluation. The winner of this year dramatically improved the EER from 2.64% to 0.483% compared to the 'winner of last year. In this paper, we introduce the process and results of ICFVR 2017 and give insights on development of state-of-art finger vein recognition algorithms.