The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
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Automatic speech recognition using acoustic doppler signal

초음파 도플러를 이용한 음성 인식

  • Lee, Ki-Seung (Department of Electronic Engineering, Konkuk University)
  • Received : 2015.07.21
  • Accepted : 2015.09.17
  • Published : 2016.01.31
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Abstract

In this paper, a new automatic speech recognition (ASR) was proposed where ultrasonic doppler signals were used, instead of conventional speech signals. The proposed method has the advantages over the conventional speech/non-speech-based ASR including robustness against acoustic noises and user comfortability associated with usage of the non-contact sensor. In the method proposed herein, 40 kHz ultrasonic signal was radiated toward to the mouth and the reflected ultrasonic signals were then received. Frequency shift caused by the doppler effects was used to implement ASR. The proposed method employed multi-channel ultrasonic signals acquired from the various locations, which is different from the previous method where single channel ultrasonic signal was employed. The PCA(Principal Component Analysis) coefficients were used as the features of ASR in which hidden markov model (HMM) with left-right model was adopted. To verify the feasibility of the proposed ASR, the speech recognition experiment was carried out the 60 Korean isolated words obtained from the six speakers. Moreover, the experiment results showed that the overall word recognition rates were comparable with the conventional speech-based ASR methods and the performance of the proposed method was superior to the conventional signal channel ASR method. Especially, the average recognition rate of 90 % was maintained under the noise environments.

본 논문에서는 음성 신호 대신 초음파 도플러 신호를 이용하여 음성을 인식하는 새로운 음성 인식 방법을 제안하였다. 제안된 방법은 주변 잡음에 대한 강인성과 무 접촉식 센서 사용에 따른 사용자의 불편함 감소를 포함하는 기존의 음성/무음성 인식 방법에 비해 몇 가지 장점을 갖는다. 제안된 방법에서는 40 kHz의 주파수를 갖는 초음파 신호를 입 주변에 방사하여, 반사된 신호를 취득하고, 취득된 신호의 도플러 주파수 변화를 이용하여 음성 인식을 구현하였다. 단일 채널 초음파 신호를 사용하는 기존의 연구와 달리, 다양한 위치에서의 취득된 초음파 신호를 음성 인식에 사용하기 위해 다채널 취득 장치를 고안하였다. PCA(Principal Component Analysis)특징 변수를 사용한 음성 인식에는 좌-우 모델을 갖는 은닉 마코프 모델을 사용하였다. 제안된 방법의 검증을 위해 60개의 한국어 고립어에 대해 6명의 화자로부터 취득된 초음파 도플러 신호를 인식에 사용하였으며, 기존 음성기반 음성인식 기법과 비교할 만한 수준의 인식율을 얻을 수 있었다. 또한 실험 결과 제안된 방법은 기존의 단일 채널 음성 인식 방법과 비교하여 우수한 성능을 나타내었으며, 특히 잡음 환경에서도 90 % 이상의 인식율을 얻을 수 있었다.

Keywords

References

  1. B. Denby, T. Schultz, K. Honda, T. Hueber, J. M. Gilbert, and J. S. Brumberg, "Silent speech interfaces," Speech Comm. 52, 270-287 (2010). https://doi.org/10.1016/j.specom.2009.08.002
  2. K.-S. Lee, "EMG-based speech recognition using Hidden Markov Models with global control variables," IEEE Trans. on Biomed. Eng. 55, 930-940 (2008). https://doi.org/10.1109/TBME.2008.915658
  3. T. Toda and K. Shikano, "NAM-to-speech conversion with gaussian mixture models," in Proc. Interspeech, 1957-1960 (2005).
  4. R. Hope, S. R. Ell, M. J. Fagan, J. M. Gilbert, P. D. Green, R. K. Moore, and S. I. Rybchenko, "Small-vocabulary speech recognition using a silent speech interface based on magnetic sensing," Speech Comm. 55, 22-32 (2013). https://doi.org/10.1016/j.specom.2012.02.001
  5. T. Hueber, G. Chollet, B. Denby, G. Dreyfus, and M. Stone, "Continuous-speech phone recognition from ultrasound and optical images of the tongue and lips," in Proc. Interspeech, 658-661 (2007).
  6. M. Jiao, G. Lu, X. Jing, S. Li, Y. Li, and J. Wang, "A novel radar sensor for the non-contact detection of speech signals," Sensors 10, 4622-4633 (2010). https://doi.org/10.3390/s100504622
  7. S. Srinivasan, B. Raj, and T. Ezzat, "Ultrasonic sensing for robust speech recognition," in Proc. ICASSP, 5102-5105 (2010).
  8. K. Kalgaonkar and B. Raj, "An acoustic doppler-based front end for hands free spoken user interfaces," in Proc. SLT, 158-161 (2006).
  9. K. Kalgaonkar and B. Raj, "Acoustic doppler sonar for gait recognition," in Proc. 2007 IEEE Conf. Advanced Video and Signal Based Surveillance, 27-32 (2007).
  10. K. Kalgaonkar and B. Raj, "One-handed gesture recognition using ultrasonic doppler sonar," Proc. ICASSP, 1889-1892 (2009).
  11. K. Kalgaonkar, R. Hu, and B. Raj, "Ultrasonic doppler sensor for voice activity detection," IEEE Signal Process. Lett. 14, 754-757 (2007). https://doi.org/10.1109/LSP.2007.896450
  12. K. Kalgaonkar and B. Raj, "Ultrasonic doppler sensor for speaker recognition," in Proc. ICASSP, 4865-4868 (2008).
  13. K. Livescu, B. Zhu, and J. Glass, "On the phonetic information in ultrasonic microphone signals," in Proc. ICASSP, 4621-4624 (2009).
  14. A. R. Toth, B. Raj, K. Kalgaonkar, and T. Ezzat, "Synthesizing speech from doppler signals," in Proc. ICASSP, 4638-4641 (2010).
  15. L. R. Rabiner and B. H. Juang, Fundamentals of speech recognition (Prentice-Hall, New Jersey, 1993), pp. 69-83.