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  2. 黒岩 眞吾

黒岩 眞吾

クロイワ シンゴ  (Shingo Kuroiwa)

基本情報

所属
千葉大学 大学院工学研究院 教授
学位
博士(電気通信大学大学院電気通信学研究科電子工学専攻)
研究者番号
20333510
J-GLOBAL ID
200901017262764603
researchmap会員ID
1000356498
外部リンク

研究キーワード

 5

研究分野

 2

経歴

 1

受賞

 7
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論文

 136
  • Satoshi Naito, Masafumi Nishimura, Masafumi Nishida, Yasuo Horiuchi, Shingo Kuroiwa
    GCCE 2024 - 2024 IEEE 13th Global Conference on Consumer Electronics 119-120 2024年  
    Obese and overweight individuals are at high risk for chronic diseases such as sleep apnea and diabetes. Therefore, it is necessary to track eating behavior to determine the causes of obesity; however, it is time- and labor-intensive to follow the lives of specific individuals and observe their eating behavior. Thus, a method to automatically monitor eating behavior should be considered. As one approach to monitoring methods, we propose a method for convenient recognition of food category for food intake sounds recorded by microphones (below the ear microphone, throat microphone and acoustic microphone), which is less burdensome to the body and better from the viewpoint of privacy protection. Furthermore, a comparison of MFB and large-scale pre-trained speech models (wav2vec2.0, wavLM, and HuBERT) showed the effectiveness of large-scale pre-trained speech models in the food recognition task.
  • Kentaro Kameda, Satoru Tsuge, Shingo Kuroiwa, Yasuo Horiuchi, Masafumi Nishida
    GCCE 2024 - 2024 IEEE 13th Global Conference on Consumer Electronics 808-810 2024年  
    To enhance speaker verification for short utterances, we have developed a Same Speaker Identification Deep Neural Network (SSI-DNN). This network identifies whether two utterances are uttered by the same speaker with greater accuracy by focusing on the same texts. In this paper, we extend the detection target of the SSI-DNN from monosyllabic utterances to word utterances to improve the speaker recognition performance. Experimental results showed that the SSI-DNN trained on word utterances achieved an EER of 0.1% to 2.8%. These results indicated that the SSI-DNN outperformed the x-vector-based speaker verification method, which is a representative speaker verification method.
  • Takumi Uehara, Shingo Kuroiwa, Yasuo Horiuchi, Masafumi Nishida, Satoru Tsuge
    GCCE 2024 - 2024 IEEE 13th Global Conference on Consumer Electronics 141-143 2024年  
    Hands-free control of shower settings, such as temperature, is highly desirable, enhancing user convenience when both hands are occupied or eyes are closed. In this paper, we propose a speaker-dependent, template-based isolated word recognition system using pre-trained large speech models (LSMs) to realize voice-activated shower control with a single microphone. Specifically, we examine the performance of 3 LSMs (wav2vec2.0, HuBERT, WavLM) as well as conventional MFCC as features. Additionally, we investigate speech enhancement using a Convolutional Recurrent Neural Network (CRN) to improve robustness against shower noise. Our experiments for recognizing 30 words with SNRs ranging from -5 dB to 20 dB demonstrate that HuBERT achieves the highest recognition accuracy (77.8 to 95.6%). CRN, on the other hand, improved recognition accuracy only under -5 dB conditions, but its accuracy was only 80.8%.
  • Hibiki Takayama, Masafumi Nishida, Satoru Tsuge, Shingo Kuroiwa
    GCCE 2024 - 2024 IEEE 13th Global Conference on Consumer Electronics 805-807 2024年  
    Recent advances in AI technology have brought not only many benefits but also considerable risks due to malicious use of the technology. One key example is spoofing through speech synthesis and voice conversion technologies against speaker verification system. To tackle this challenge, we proposed a two-step matching method as a robust speaker verification, in which a user specifies an emotion to a system in advance, and the user is accepted only when the user speaks with the specified emotion. This previous method reduced the false acceptance rate. However, the false rejection rate increased. To overcome this problem, we propose a novel method that integrates speaker and emotion verification scores in this work. Experiments revealed that the proposed method can reduce the equal error rate compared with that of the conventional method to assign the optimal weight to the speaker and emotional information contained in the speech.
  • Hibiki Takayama, Masafumi Nishida, Satoru Tsuge, Shingo Kuroiwa, Masafumi Nishimura
    2023 IEEE 12th Global Conference on Consumer Electronics (GCCE) 2023年10月10日  
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MISC

 588

講演・口頭発表等

 30
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所属学協会

 4

Works(作品等)

 5

共同研究・競争的資金等の研究課題

 19
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産業財産権

 48
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