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Convolutional Neural Networks are widely used in various machine learning domains. In image processing, the features can be obtained by applying 2D convolution to all spatial dimensions of the input. However, in the audio case, frequency domain input like Mel-Spectrogram has different and unique characteristics in the frequency dimension. Thus, there is a need for a method that allows the 2D convolution layer to handle the frequency dimension differently. In this work, we introduce SubSpectral Normalization (SSN), which splits the input frequency dimension into several groups (sub-bands) and performs a different normalization for each group. SSN also includes an affine transformation that can be applied to each group. Our method removes the inter-frequency deflection while the network learns a frequency-aware characteristic. In the experiments with audio data, we observed that SSN can efficiently improve the network's performance., Comment: 4 pages, ICASSP '21 accepted

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.

Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA), independent component analysis (ICA), and linear discriminant analysis (LDA). Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS) based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.

In our previous work, the use of PCA instead of DCT shows robustness in distorted speech recognition because the main speech element is projected onto low-order features, while the noise or distortion element is projected onto high-order features [1]. This paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and their elements are statistically mutual independent. The proposed speech feature vector is generated by projecting observed vector onto integrated space obtained by PCA and ICA. The performance evaluation shows that the proposed method provides a higher isolated word recognition accuracy than conventional methods in some reverberant conditions.