黒岩 眞吾

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.

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.

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%.

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.