Qingqing Zhang

Gaofeng Cheng, Yifan Chen, Runyan Yang et al.

The conversation scenario is one of the most important and most challenging scenarios for speech processing technologies because people in conversation respond to each other in a casual style. Detecting the speech activities of each person in a conversation is vital to downstream tasks, like natural language processing, machine translation, etc. People refer to the detection technology of "who speak when" as speaker diarization (SD). Traditionally, diarization error rate (DER) has been used as the standard evaluation metric of SD systems for a long time. However, DER fails to give enough importance to short conversational phrases, which are short but important on the semantic level. Also, a carefully and accurately manually-annotated testing dataset suitable for evaluating the conversational SD technologies is still unavailable in the speech community. In this paper, we design and describe the Conversational Short-phrases Speaker Diarization (CSSD) task, which consists of training and testing datasets, evaluation metric and baselines. In the dataset aspect, despite the previously open-sourced 180-hour conversational MagicData-RAMC dataset, we prepare an individual 20-hour conversational speech test dataset with carefully and artificially verified speakers timestamps annotations for the CSSD task. In the metric aspect, we design the new conversational DER (CDER) evaluation metric, which calculates the SD accuracy at the utterance level. In the baseline aspect, we adopt a commonly used method: Variational Bayes HMM x-vector system, as the baseline of the CSSD task. Our evaluation metric is publicly available at https://github.com/SpeechClub/CDER_Metric.

Shuhao Deng, Chengfei Li, Jinfeng Bai et al.

Code-switching automatic speech recognition becomes one of the most challenging and the most valuable scenarios of automatic speech recognition, due to the code-switching phenomenon between multilingual language and the frequent occurrence of code-switching phenomenon in daily life. The ISCSLP 2022 Chinese-English Code-Switching Automatic Speech Recognition (CSASR) Challenge aims to promote the development of code-switching automatic speech recognition. The ISCSLP 2022 CSASR challenge provided two training sets, TAL_CSASR corpus and MagicData-RAMC corpus, a development and a test set for participants, which are used for CSASR model training and evaluation. Along with the challenge, we also provide the baseline system performance for reference. As a result, more than 40 teams participated in this challenge, and the winner team achieved 16.70% Mixture Error Rate (MER) performance on the test set and has achieved 9.8% MER absolute improvement compared with the baseline system. In this paper, we will describe the datasets, the associated baselines system and the requirements, and summarize the CSASR challenge results and major techniques and tricks used in the submitted systems.

Kangxiang Xia, Dake Guo, Jixun Yao et al.

The ISCSLP 2024 Conversational Voice Clone (CoVoC) Challenge aims to benchmark and advance zero-shot spontaneous style voice cloning, particularly focusing on generating spontaneous behaviors in conversational speech. The challenge comprises two tracks: an unconstrained track without limitation on data and model usage, and a constrained track only allowing the use of constrained open-source datasets. A 100-hour high-quality conversational speech dataset is also made available with the challenge. This paper details the data, tracks, submitted systems, evaluation results, and findings.

Zehui Yang, Yifan Chen, Lei Luo et al.

This paper introduces a high-quality rich annotated Mandarin conversational (RAMC) speech dataset called MagicData-RAMC. The MagicData-RAMC corpus contains 180 hours of conversational speech data recorded from native speakers of Mandarin Chinese over mobile phones with a sampling rate of 16 kHz. The dialogs in MagicData-RAMC are classified into 15 diversified domains and tagged with topic labels, ranging from science and technology to ordinary life. Accurate transcription and precise speaker voice activity timestamps are manually labeled for each sample. Speakers' detailed information is also provided. As a Mandarin speech dataset designed for dialog scenarios with high quality and rich annotations, MagicData-RAMC enriches the data diversity in the Mandarin speech community and allows extensive research on a series of speech-related tasks, including automatic speech recognition, speaker diarization, topic detection, keyword search, text-to-speech, etc. We also conduct several relevant tasks and provide experimental results to help evaluate the dataset.

Hao Li, Aozhou Wu, Wen Fang et al.

Resonant Beam Charging (RBC) is a wireless charging technology which supports multi-watt power transfer over meter-level distance. The features of safety, mobility and simultaneous charging capability enable RBC to charge multiple mobile devices safely at the same time. To detect the devices that need to be charged, a Mask R-CNN based dection model is proposed in previous work. However, considering the constraints of the RBC system, it's not easy to apply Mask R-CNN in lightweight hardware-embedded devices because of its heavy model and huge computation. Thus, we propose a machine learning detection approach which provides a lighter and faster model based on traditional Mask R-CNN. The proposed approach makes the object detection much easier to be transplanted on mobile devices and reduce the burden of hardware computation. By adjusting the structure of the backbone and the head part of Mask R-CNN, we reduce the average detection time from $1.02\mbox{s}$ per image to $0.6132\mbox{s}$, and reduce the model size from $245\mbox{MB}$ to $47.1\mbox{MB}$. The improved model is much more suitable for the application in the RBC system.

Qingqing Zhang, Wen Fang, Mingliang Xiong et al.

As a long-range high-power wireless power transfer (WPT) technology, resonant beam charging (RBC) can transmit Watt-level power over long distance for the devices in the internet of things (IoT). Due to its open-loop architecture, RBC faces the challenge of providing dynamic current and voltage to optimize battery charging performance. In RBC, battery overcharge may cause energy waste, thermal effects, and even safety issues. On the other hand, battery undercharge may lead to charging time extension and significant battery capacity reduction. In this paper, we present an adaptive resonant beam charging (ARBC) system for battery charging optimization. Based on RBC, ARBC uses a feedback system to control the supplied power dynamically according to the battery preferred charging values. Moreover, in order to transform the received current and voltage to match the battery preferred charging values, ARBC adopts a direct current to direct current (DC-DC) conversion circuit. Relying on the analytical models for RBC power transmission, we obtain the end-to-end power transfer relationship in the approximate linear closed-form of ARBC. Thus, the battery preferred charging power at the receiver can be mapped to the supplied power at the transmitter for feedback control. Numerical evaluation demonstrates that ARBC can save 61% battery charging energy and 53%-60% supplied energy compared with RBC. Furthermore, ARBC has high energy-saving gain over RBC when the WPT is unefficient. ARBC in WPT is similar to link adaption in wireless communications. Both of them play the important roles in their respective areas.

Qingqing Zhang, Mingqing Liu, Xing Lin et al.

To enable electric vehicles (EVs) to access to the internet of intelligent vehicles (IoIV), charging EVs wirelessly anytime and anywhere becomes an urgent need. The resonant beam charging (RBC) technology can provide high-power and long-range wireless energy for EVs. However, the RBC system is unefficient. To improve the RBC power transmission efficiency, the adaptive resonant beam charging (ARBC) technology was introduced. In this paper, after analyzing the modular model of the ARBC system, we obtain the closed-form formula of the end-to-end power transmission efficiency. Then, we prove that the optimal power transmission efficiency uniquely exists. Moreover, we analyze the relationships among the optimal power transmission efficiency, the source power, the output power, and the beam transmission efficiency, which provide the guidelines for the optimal ARBC system design and implementation. Hence, perpetual energy can be supplied to EVs in IoIV virtually.

Wen Fang, Qingqing Zhang, Qingwen Liu et al.

Resonant Beam Charging (RBC) is the Wireless Power Transfer (WPT) technology, which can provide high-power, long-distance, mobile, and safe wireless charging for Internet of Things (IoT) devices. Supporting multiple IoT devices charging simultaneously is a significant feature of the RBC system. To optimize the multi-user charging performance, the transmitting power should be scheduled for charging all IoT devices simultaneously. In order to keep all IoT devices working as long as possible for fairness, we propose the First Access First Charge (FAFC) scheduling algorithm. Then, we formulate the scheduling parameters quantitatively for algorithm implementation. Finally, we analyze the performance of FAFC scheduling algorithm considering the impacts of the receiver number, the transmitting power and the charging time. Based on the analysis, we summarize the methods of improving the WPT performance for multiple IoT devices, which include limiting the receiver number, increasing the transmitting power, prolonging the charging time and improving the single-user's charging efficiency. The FAFC scheduling algorithm design and analysis provide a fair WPT solution for the multi-user RBC system.

Wen Fang, Qingqing Zhang, Mingqing Liu et al.

Resonant Beam Charging (RBC) is a promising Wireless Power Transfer (WPT) technology to provide long-range, high-power, mobile and safe wireless power for the Internet of Things (IoT) devices. The Point-to-Multipoint (PtMP) RBC system can charge multiple receivers simultaneously similar to WiFi communications. To guarantee the Quality of Charging Service (QoCS) for each receiver and maximize the overall earning in the PtMP RBC service, we specify the Charging Pricing Strategy (CPS) and develop the High Priority Charge (HPC) scheduling algorithm to control the charging order and power allocation. Each receiver is assigned a priority, which is updated dynamically based on its State of Charging (SOC) and specified charging power. The receivers with high priorities are scheduled to be charged in each time slot. We present the pseudo code of the HPC algorithm based on quantifying the receiver's SOC, discharging energy and various relevant parameters. Relying on simulation analysis, we demonstrate that the HPC algorithm can achieve better QoCS and earning than the Round-Robin Charge (RRC) scheduling algorithm. Based on the performance evaluation, we illustrate that the methods to improve the PtMP RBC service are: 1) limiting the receiver number within a reasonable range and 2) prolonging the charging duration as long as possible. In summary, the HPC scheduling algorithm provides a practical strategy to maximize the earning of the PtMP RBC service with each receiver's QoCS guarantee.