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.
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.
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%.
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.
ELECTRONICS AND COMMUNICATIONS IN JAPAN 99(11) 80-89 2016年11月
In this paper, we propose a speaker indexing method using speaker verification technique to extract one desired speaker's utterances from conversational speech. To solve the overlapped speech problem, we construct overlapped speech models with the observed conversational speech itself. The overlapped speech models include overlapped speech of target and cohort speaker, and speech model of two cohort speakers. In order to evaluate the proposed method, we made a simulated conversational speech that has up to 50% overlapping segments. The equal error rate was reduced by up to 43.7% compared with the conventional methods that use a target speaker model only, and use a target model and overlapped speech model trained with a speaker independent of large speech database. (C). 2016 Wiley Periodicals, Inc.
In this paper, we propose a speaker indexing method using speaker verification technique to extract one desired speaker's utterances from conversational speech. To solve the overlapped speech problem, we construct overlapped speech models with the observed conversational speech itself. The overlapped speech models include overlapped speech of target and cohort speaker, and speech model of two cohort speakers. In order to evaluate the proposed method, we made a simulated conversational speech that has up to 50% overlapping segments. The EER was reduced by up to 43.7% compared with the conventional methods that use a target speaker model only, and use a target model and overlapped speech model trained with a speaker independent large speech database.