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This paper presents a speech recognition system developed by the Transsion Speech Understanding Processing Team (TSUP) for the ASRU 2023 MADASR Challenge. The system focuses on adapting ASR models for low-resource Indian languages and covers all four tracks of the challenge. For tracks 1 and 2, the acoustic model utilized a squeezeformer encoder and bidirectional transformer decoder with joint CTC-Attention training loss. Additionally, an external KenLM language model was used during TLG beam search decoding. For tracks 3 and 4, pretrained IndicWhisper models were employed and finetuned on both the challenge dataset and publicly available datasets. The whisper beam search decoding was also modified to support an external KenLM language model, which enabled better utilization of the additional text provided by the challenge. The proposed method achieved word error rates (WER) of 24.17%, 24.43%, 15.97%, and 15.97% for Bengali language in the four tracks, and WER of 19.61%, 19.54%, 15.48%, and 15.48% for Bhojpuri language in the four tracks. These results demonstrate the effectiveness of the proposed method.

This paper describes the TSUP team's submission to the ISCSLP 2022 conversational short-phrase speaker diarization (CSSD) challenge which particularly focuses on short-phrase conversations with a new evaluation metric called conversational diarization error rate (CDER). In this challenge, we explore three kinds of typical speaker diarization systems, which are spectral clustering(SC) based diarization, target-speaker voice activity detection(TS-VAD) and end-to-end neural diarization(EEND) respectively. Our major findings are summarized as follows. First, the SC approach is more favored over the other two approaches under the new CDER metric. Second, tuning on hyperparameters is essential to CDER for all three types of speaker diarization systems. Specifically, CDER becomes smaller when the length of sub-segments setting longer. Finally, multi-system fusion through DOVER-LAP will worsen the CDER metric on the challenge data. Our submitted SC system eventually ranks the third place in the challenge.

Both thallium (Tl) and arsenic (As) bear severe toxicity. Brake fern (Pteris vittata L.) is well-known for its hyperaccumulation capacity of As, yet its role on Tl accumulation remains unknown. Herein, brake ferns growing near an As tailing site in Yunnan, Southwestern China are for the first time discovered as a co-hyperaccumulator of both Tl and As. The results showed that the brake ferns extracted both As and Tl efficiently from the soils into the fronds. The studied ferns growing on Tl and As co-polluted soils were found to accumulate extremely high levels of both As (7215–11110 mg/kg) and Tl (6.47–111 mg/kg). Conspicuously high bio-accumulation factor (BCF) was observed for As (7.8) and even higher for Tl (28.4) among these co-hyperaccumulators, wherein the contents of As and Tl in contaminated soils were 1240 ± 12 and 3.91 ± 0.01 mg/kg, respectively. The applied advanced characterization techniques (e.g. transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS)) indicated a preferential uptake of Tl(I) while simultaneous accumulation of As (III) and As(V) from the Tl(I)/Tl(III)-As (III)/As(V) co-existent rhizospheric soils. The findings benefit the phytoremediation practice and pose implications for managing and restoring Tl-As co-contaminated soils in other countries.