Streaming Speaker-Attributed ASR with Token-Level Speaker Embeddings

This paper presents a streaming speaker-attributed automatic speech recognition (SA-ASR) model that can recognize "who spoke what" with low latency even when multiple people are speaking simultaneously. Our model is based on token-level serialized output training (t-SOT) which was recently proposed to transcribe multi-talker speech in a streaming fashion. To further recognize speaker identities, we propose an encoder-decoder based speaker embedding extractor that can estimate a speaker representation for each recognized token not only from non-overlapping speech but also from overlapping speech. The proposed speaker embedding, named t-vector, is extracted synchronously with the t-SOT ASR model, enabling joint execution of speaker identification (SID) or speaker diarization (SD) with the multi-talker transcription with low latency. We evaluate the proposed model for a joint task of ASR and SID/SD by using LibriSpeechMix and LibriCSS corpora. The proposed model achieves substantially better accuracy than a prior streaming model and shows comparable or sometimes even superior results to the state-of-the-art offline SA-ASR model.Comment: Submitted to Interspeech 202

Critical role of the gut microbiota in immune responses and cancer immunotherapy

The gut microbiota plays a critical role in the progression of human diseases, especially cancer. In recent decades, there has been accumulating evidence of the connections between the gut microbiota and cancer immunotherapy. Therefore, understanding the functional role of the gut microbiota in regulating immune responses to cancer immunotherapy is crucial for developing precision medicine. In this review, we extract insights from state-of-the-art research to decipher the complicated crosstalk among the gut microbiota, the systemic immune system, and immunotherapy in the context of cancer. Additionally, as the gut microbiota can account for immune-related adverse events, we discuss potential interventions to minimize these adverse effects and discuss the clinical application of five microbiota-targeted strategies that precisely increase the efficacy of cancer immunotherapy. Finally, as the gut microbiota holds promising potential as a target for precision cancer immunotherapeutics, we summarize current challenges and provide a general outlook on future directions in this field

WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing

Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. To tackle the problem, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM jointly learns masked speech prediction and denoising in pre-training. By this means, WavLM does not only keep the speech content modeling capability by the masked speech prediction, but also improves the potential to non-ASR tasks by the speech denoising. In addition, WavLM employs gated relative position bias for the Transformer structure to better capture the sequence ordering of input speech. We also scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks. The code and pre-trained models are available at https://aka.ms/wavlm.Comment: Submitted to the Journal of Selected Topics in Signal Processing (JSTSP

A novel IgE epitope-specific antibodies-based sandwich ELISA for sensitive measurement of immunoreactivity changes of peanut allergen Ara h 2 in processed foods

BackgroundPeanut is an important source of dietary protein for human beings, but it is also recognized as one of the eight major food allergens. Binding of IgE antibodies to specific epitopes in peanut allergens plays important roles in initiating peanut-allergic reactions, and Ara h 2 is widely considered as the most potent peanut allergen and the best predictor of peanut allergy. Therefore, Ara h 2 IgE epitopes can serve as useful biomarkers for prediction of IgE-binding variations of Ara h 2 and peanut in foods. This study aimed to develop and validate an IgE epitope-specific antibodies (IgE-EsAbs)-based sandwich ELISA (sELISA) for detection of Ara h 2 and measurement of Ara h 2 IgE-immunoreactivity changes in foods.MethodsDEAE-Sepharose Fast Flow anion-exchange chromatography combining with SDS-PAGE gel extraction were applied to purify Ara h 2 from raw peanut. Hybridoma and epitope vaccine techniques were employed to generate a monoclonal antibody against a major IgE epitope of Ara h 2 and a polyclonal antibody against 12 IgE epitopes of Ara h 2, respectively. ELISA was carried out to evaluate the target binding and specificity of the generated IgE-EsAbs. Subsequently, IgE-EsAbs-based sELISA was developed to detect Ara h 2 and its allergenic residues in food samples. The IgE-binding capacity of Ara h 2 and peanut in foods was determined by competitive ELISA. The dose-effect relationship between the Ara h 2 IgE epitope content and Ara h 2 (or peanut) IgE-binding ability was further established to validate the reliability of the developed sELISA in measuring IgE-binding variations of Ara h 2 and peanut in foods.ResultsThe obtained Ara h 2 had a purity of 94.44%. Antibody characterization revealed that the IgE-EsAbs recognized the target IgE epitope(s) of Ara h 2 and exhibited high specificity. Accordingly, an IgE-EsAbs-based sELISA using these antibodies was able to detect Ara h 2 and its allergenic residues in food samples, with high sensitivity (a limit of detection of 0.98 ng/mL), accuracy (a mean bias of 0.88%), precision (relative standard deviation < 16.50%), specificity, and recovery (an average recovery of 98.28%). Moreover, the developed sELISA could predict IgE-binding variations of Ara h 2 and peanut in foods, as verified by using sera IgE derived from peanut-allergic individuals.ConclusionThis novel immunoassay could be a user-friendly method to monitor low level of Ara h 2 and to preliminary predict in vitro potential allergenicity of Ara h 2 and peanut in processed foods