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Herein, we report a new platform of machine learning-based speaker recognition via the flexible piezoelectric acoustic sensor (f-PAS) with a highly sensitive multi-resonant frequency band. The resonant self-powered f-PAS was fabricated by mimicking the operating mechanism of the basilar membrane in the human cochlear. The f-PAS acquired abundant voice information from the multi-channel sound inputs. The standard TIDIGITS dataset were recorded by the f-PAS and converted to frequency components by using a Fast Fourier Transform (FFT) and a Short-Time Fourier Transform (STFT). The machine learning based Gaussian Mixture Model (GMM) was designed by utilizing the most highest and second highest sensitivity data among multi-channel outputs, exhibiting outstanding speaker recognition rate of 97.5% with error rate reduction of 75% compared to that of the reference MEMS microphone.

Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface for artificial intelligence (AI) services. Collaboration and novel approaches between both smart sensors and speech algorithms should be attempted to realize a hyperconnected society, which can offer personalized services such as biometric authentication, AI secretaries, and home appliances. Here, representative developments in speech recognition are reviewed in terms of flexible piezoelectric materials, self-powered sensors, machine learning algorithms, and speaker recognition.

This paper considers a tied triphone semi-Markov model (SMM) for large vocabulary continuous speech recognition (LVCSR). The semi-Markov model (SMM) can represent statistical dependencies among all the observations within a segment while hidden Markov model (HMM) that is often used for speech recognition assumes only local statistical dependencies between adjacent observations. Especially, this paper focuses on tied triphone construction of SMM for LVCSR. The proposed tied triphone SMM outperformed the HMM in the WSJ speech recognition task.

This paper considers blind speech separation and recognition for human robot interaction in reverberant environment. In the proposed system, the speech sources whose number is more than the number of mixtures are separated by MMSE-criteria and recognized by HMM-based speech recognizer. Experimental result shows the superiority of the proposed system.

This paper describes an emotional singing voice synthesis algorithm based on hidden Markov model. To express a particular emotion, the fundamental frequency (F0) contour of singing voice is modified by controlling the parameters consisting of vibrato extent, vibrato rate, overshoot, and preparation. Experimental results on the effectiveness of F0 contour modification in expressing a certain emotion is described. We have found that F0 contour modification is effective in expressing a “sad” emotion but not effective in expressing a “happy” emotion.