In recent years, there has been a demand for advanced maintenance in factories. Data collection from factory equipment is being carried out, and the collected sensor data is widely used for statistical analysis in quality control and failure prediction by machine learning. For example, if it is possible to detect an abnormality using vibration data obtained from an equipment, increase in the operation rate of the plant can be expected. In this research, we aim at early detection of equipment failure by finding signs of abnormality from vibration data, using a deep-learning technique, particularly an autoencoder. In this paper, the following two methods were tested. The first scheme is based on the reconstruction error in an autoencoder. An autoencoder is trained using normal data only. Looking at the difference between input data and reconstructed data, we can regard the data having higher difference as abnormal. In the second approach, given the input data, values of the middle layer of the autoencoder are extracted, and we calculate the degree of abnormality using a Gaussian Mixture Model (GMM), representing a data set by superposition of a mixture of Gaussian distributions. In this framework, regarding an autoencoder structure, we tested both full-connection networks and convolutional networks. In this work, we chose a press machine. Frequency characteristics were acquired from the data in production mode of a press machine. Then using each method, we evaluated whether abnormality could be found by calculating the degree of abnormality. We employed two-day data without failure as training data, and another data set was prepared as forecast data obtained on the following days
on one of the days the machine stopped due to a sudden abnormality. Similar to time-series signal processing, we applied framing processing so that we can analyze data even in the case we can only get a small amount of data. As a result, our method succeeded in finding the day when the abnormality occurred and the machine stopped. In addition, the degree of abnormality became higher before the abnormality occurs, indicating we can detect signs of abnormality. In conclusion, the degree of abnormality could be calculated using the reconstruction error using an autoencoder from the vibration data during production, and the method using GMM from the middle layer of autoencoder. We consequently conclude it is possible to detect a sudden abnormality in which the device stopped, from actual vibration data. These results provide new solutions for equipment failure estimation.

In piano learning, it is difficult especially for beginners to judge by themselves whether their musical performances are appropriate in terms of rhythm and melody. Therefore, we have been developing a piano practice support system, which enables piano beginners to conduct independent practice without their instructors. In this paper, we propose the system with the aid of a deep learning technique: Long Short-Term Memory (LSTM). Our system accepts raw piano sounds, extracting performance information. From these information, we evaluate performance. We evaluated the scheme using actual beginners' performances, and found the proposed system achieved better than previous conventional methods. This paper also presents an application employing our methods. Through subjective evaluation experiments for the proposed application, it turns out almost the all beginners found reflection points, and they maintained their motivation for independent practice.

Image processing techniques have been actively used for research on road condition inspection and achieving high detection accuracies. Many studies focus on the detection of cracks and potholes of the road. However, in some least developed countries, there are some distances of roads are still unpaved and it escaped the attention of the researchers. Inspired by penetration and success in applying deep learning technic to computer vision and to any other fields and by the existence of the various type of smartphone devices, we proposed a low - cost method for paved and unpaved road images classification using convolutional neural network (CNN). Our model is trained with 13.186 images and validate with 3.186 images which collected using smartphone device in various conditions of roads such as wet, muddy, dry, dusty and shady conditions and with different types of road surface such as ground, rocks and sands. The experiment using 500 new testing images showed that our model can achieve high Precision (98.0%), Recall (98.4%) and F1 - Score (98.2%) simultaneously.

This research proposes a low-cost solution for detecting road potholes image by using convolutional neural network (CNN). Our model is trained entirely on the image which collected from several different places and has variation such as in wet, dry and shady conditions. The experiment using the 500 testing images showed that our model can achieve (99.80 %) of Accuracy, Precision (100%), Recall (99.60%), and F-Measure (99.60%) simultaneously.

In the speech recognition literature, building corpora for Large Vocabulary Continuous Speech Recognition (LVCSR) is quite important. In addition, in order to overcome performance decrease caused by noise, using visual information such as lip images is effective. In this paper, therefore, we focus on collecting speech and lip-image data for audio-visual LVCSR. Audio-visual speech data were obtained from 12 speakers, each who uttered ATR503 phonetically-balanced sentences. These data were recorded in acoustically and visually clean environments. Using the data, we conducted recognition experiments. Mel Frequency Cepstral Coefficients (MFCCs) and eigenlip features were obtained, and multi-stream Hidden Markov Models (HMMs) were built. We compared the performance in clean condition to those in noisy environments. It is found that visual information is able to compensate the performance. In addition, it turns out that we should improve visual speech recognition for high-performance audio-visual LVCSR.

© 2016 The Institute of Electronics, Information and Communication Engineers. Audio-Visual Speech Recognition (AVSR) is one of techniques to enhance robustness of speech recognizer in noisy or real environments. On the other hand, Deep Neural Networks (DNNs) have recently attracted a lot of attentions of researchers in the speech recognition field, because we can drastically improve recognition performance by using DNNs. There are two ways to employ DNN techniques for speech recognition: a hybrid approach and a tandem approach; in the hybrid approach an emission probability on each Hidden Markov Model (HMM) state is computed using a DNN, while in the tandem approach a DNN is composed into a feature extraction scheme. In this paper, we investigate and compare several DNN-based AVSR methods to mainly clarify how we should incorporate audio and visual modalities using DNNs. We carried out recognition experiments using a corpus CENSREC-1-AV, and we discuss the results to find out the best DNN-based AVSR modeling. Then it turns out that a tandembased method using audio Deep Bottle-Neck Features (DBNFs) and visual ones with multi-stream HMMs is the most suitable, followed by a hybrid approach and another tandem scheme using audio-visual DBNFs.

© 2015 Asia-Pacific Signal and Information Processing Association. This paper develops an Audio-Visual Speech Recognition (AVSR) method, by (1) exploring high-performance visual features, (2) applying audio and visual deep bottleneck features to improve AVSR performance, and (3) investigating effectiveness of voice activity detection in a visual modality. In our approach, many kinds of visual features are incorporated, subsequently converted into bottleneck features by deep learning technology. By using proposed features, we successfully achieved 73.66% lipreading accuracy in speaker-independent open condition, and about 90% AVSR accuracy on average in noisy environments. In addition, we extracted speech segments from visual features, resulting 77.80% lipreading accuracy. It is found VAD is useful in both audio and visual modalities, for better lipreading and AVSR.

This paper reports our recent efforts to develop robust speech recognition in cars. Speech recognition is expected to handle many devices on cars. However, many kinds of acoustic noises, e.g. engine noise and car stereo, are observed in in-car environments, making performance of speech recognition decrease. In order to overcome the degradation, we develop a high-performance audio-visual speech recognition method. Lip images are obtained from captured face images using our face detection scheme. Some basic visual features are computed, then converted into visual features for speech recognition using a deep neural network. Audio features are obtained as well. Audio and visual features are subsequently concatenated into audio-visual features. As a recognition model, a multi-stream hidden Markov model is employed which can adjust contributions of audio and visual modalities. We evaluated our proposed method using an audio-visual corpus CENSREC-1-AV. In order to simulate driving-car condition, we prepared driving and music noises. Experimental results show that our method can significantly improving recognition performance in in-car condition.

This paper develops an Audio-Visual Speech Recognition (AVSR) method, by (1) exploring high-performance visual features, (2) applying audio and visual deep bottleneck features to improve AVSR performance, and (3) investigating effectiveness of voice activity detection in a visual modality. In our approach, many kinds of visual features are incorporated, subsequently converted into bottleneck features by deep learning technology. By using proposed features, we successfully achieved 73.66% lipreading accuracy in speaker-independent open condition, and about 90% AVSR accuracy on average in noisy environments. In addition, we extracted speech segments from visual features, resulting 77.80% lipreading accuracy. It is found VAD is useful in both audio and visual modalities, for better lipreading and AVSR.

In service engineering it is important to estimate when and what a worker did, because they include crucial evidences to improve service quality and working environments. For Service Operation Estimation (SOE), acoustic information is one of useful and key modalities; particularly environmental or background sounds include effective cues. This paper focuses on two aspects: (1) extracting powerful and robust acoustic features by using stacked-denoising-autoencoder and hag-of-feature techniques, and (2) investigating a multi-modal SOE scheme by combining the audio features and the other sensor data as well as non-sensor information. We conducted evaluation experiments using multi-modal data recorded in a restaurant. We improved SOE performance in comparison to conventional acoustic features, and effectiveness of our multi modal SOE scheme is also clarified.

In this paper, we propose to use staying area data toward the estimation of serving time for customers. To classify utterances enables us to estimate conversation types between speakers. However, its performance becomes lower in real environments. We propose a method using area data with sound data to solve this problem. We also propose a method to estimate the conversation types using the decision trees. They were tested with the data recorded in a Japanese restaurant. In the experiment to classify utterances, the proposed method performed better than the method using only sound data. In the experiment to estimate the conversation types, we succeeded to recover 70% of the mis-classified conversations using both of sound and area data.

Human behavior sensing and their analysis are great role to improve service quality and education of employees. This paper shows novel frameworks of detection of customer communication and lead time estimation(LTE) by using multi-sensored data, sound data and accounting data in the restaurant. They are useful for management about work environments and problems for employees. Lead time from order to delivery shows the quality of the service for customers. We found sound data of an employee's speech is useful for these techniques by speech ratio smoothing and POS sound detection.

In this paper, we propose to use exemplar-based sparse representation features for noise robust audio-visual speech recognition. First, we introduce a sparse representation technology and describe how noise robustness can be realized by the sparse representation for noise reduction. Then, feature fusion methods are proposed to combine audio-visual features with the sparse representation. Our work provides new insight into two crucial issues in automatic speech recognition: noise reduction and robust audio-visual features. For noise reduction, we describe a noise reduction method in which speech and noise are mapped into different subspaces by the sparse representation to reduce the noise. Our proposed method can be deployed not only on audio noise reduction but also on visual noise reduction for several types of noise. For the second issue, we investigate two feature fusion methods –- late feature fusion and the joint sparsity model method –- to calculate audio-visual sparse representation features to improve the accuracy of the audio-visual speech recognition. Our proposed method can also contribute to feature fusion for the audio-visual speech recognition system. Finally, to evaluate the new sparse representation features, a database for audio-visual speech recognition is used in this research. We show the effectiveness of our proposed noise reduction on both audio and visual cases for several types of noise and the effectiveness of audio-visual feature determination by the joint sparsity model, in comparison with the late feature fusion method and traditional methods.

Voice Conversion (VC) is a technique to convert speech data of source speaker into ones of target speaker. VC has been investigated and statistical VC is used for various purposes. Conventional VC uses acoustic features, however, the audio-only VC has suffered from the degradation in noisy or real environments. This paper proposes an Audio-Visual VC (AVVC) method using not only audio features but also visual information, i.e. lip images. Eigenlip feature is employed in our scheme as visual feature. We also propose a feature selection approach for audio-visual features. Experiments were conducted to evaluate our AVVC scheme comparing with audio-only VC, using noisy data. The results show that AVVC can improve the performance even in noisy environments, by properly selecting audio and visual parameters. It is also found that visual VC is also successful. Furthermore, it is observed that visual dynamic features are more effective than visual static information.

This paper introduces our recent activities for audio-visual speech recognition on mobile devices and data collection in various environments. Audio-visual automatic speech recognition is effective in noisy or real conditions to enhance the robustness of speech recognizer and to improve the recognition accuracy. We have developed an audio-visual speech recognition interface for mobile devices. In order to evaluate the recognizer and investigate issues related to audio-visual processing on mobile computers, we collected speech data and lip images of 16 subjects in eight conditions, where there were various audio noises and visual difficulties. Audio-only speech recognition and visual-only lipreading were then conducted. Through these experiments, we found some issues and future works not only for construction of audio-visual database but also for robust audio-visual speech recognition.

We propose a probabilistic expression of PSI (Polynomial Semantic Indexing). PSI is a model which represents a latent semantic space in the polynomial form of input vectors. PSI express high-order relationships between more than two vectors in the form of extended inner products. PSI employs the low rank representation, which enables us to treat high-dimensional data without processes such as dimension reduction and feature extraction explicitly. Our proposed pPSI also has the same advantages as PSI. The contribution of this paper is (1) to formulate a probabilistic expression of PSI (pPSI), (2) to propose a pPSI-based classifier, and (3) to show a possibility of the pPSI classifier. The training algorithm of the stochastic gradient descendent for pPSI is introduced, saving memory use as well as computational costs. Furthermore, pPSI has a potential to reach the better solution compared to PSI. The proposed pPSI method can perform model-based training and adaptation, such as MAP (Maximum A Posterior)-based estimation according to the amount of data. In order to evaluate pPSI and its classifier, we conducted three experiments with artificial data and music data, comparing with multi-class SVM and boosting classifiers. Through the experiments, it is shown that the proposed method is feasible, especially for the case of small dimension of latent concept spaces. (c) 2013 Elsevier B.V. All rights reserved.

Lip reading technologies play a great role not only in image pattern recognition e.g. computer vision, but also in audio-visual pattern recognition e.g. bimodal speech recognition. However, it is a problem that the recognition accuracy is still significantly low, compared to that of speech recognition. Another problem lies which the performance degradation occurs in real environments. To improve the performance, in this paper we employ two adaptation schemes: speaker adaptation and environmental adaptation. The speaker adaptation is performed to recognition models so as to prevent the degradation caused by the difference of speakers. The environmental adaptation is also conducted to deal with environmental differences. We tested these adaptation schemes using a real-world audio-visual corpus CENSREC-2-AV, we have built this corpus containing real-world data (speech signals and lip images) recorded in a driving car, in which subjects uttered Japanese connected digits. Experimental results show that the lip reading recognition performance was largely improved by the speaker adaptation, and further recovered by the environmental adaptation. © 2013 IEEE.

The purpose of this study is to build an evaluation framework for robust bimodal speech recognition in real environments, such as in-car conditions. Bimodal speech recognition using lip images has been studied to prevent the deterioration of speech recognition performance in noisy environments. Lip reading technologies using lip images play a great role for the bimodal speech recognition. Therefore, for the bimodal speech recognition, a database both speech signals and lip images is necessary to build a bimodal speech recognizer and to evaluate its performance. An evaluation framework for noisy bimodal speech recognition (CENSREC-1-AV) was constructed by Tamura et al; a subject on a blue screen background spoke Japanese connected digits in a quiet office environment. CENSREC-1-AV was recorded in the clean condition, on the other hand, a database recorded in real environments is required to evaluate a bimodal speech recognizer. Therefore, we have constructed a new audio-visual corpus CENSREC-2-AV, recorded in in-car environments; a subject sitting on a driver's seat in a car uttered Japanese connected digits in various driving conditions: for example, a tunnel situation with music background noises, and driving on an expressway while the window is open. By using CENSREC-2-AV, it is possible to realize a robust bimodal speech recognition method even in real environments.

This paper proposes to use Web information for confidence measure and to extract keywords for speech recognition results. Spoken document processing has been attracting attention particularly for information retrieval and video (audiovisual) content systems. For example, measuring a confidence score which indicates how likely a document or a segmented document includes recognition errors has been studied. It is well known keyword extraction from recognition results is also an important issue. For these purposes, in this paper, pointwise mutual information (PMI) between two words is employed. PMI has been used to calculate a confidence measure of speech recognition, as a coherence measure by co-occurrence of words. We propose to further improve the method by using a Web query expansion technique with term triplets which consist of nouns in the same document. We also apply PMI to keyword estimation by summing a co-occurrence score (sumPMI) between a targeting keyword candidate and each term. The proposed methods were tested with 10 lectures in Corpus of Spontaneous Japanese (CSJ) and 2 simulated movie dialogues. In the experiments it is shown that the estimated confidence score has high relationship with recognition accuracy, indicating the effectiveness of our method. And sumPMI scores for keywords have higher values in the subjective tests.

A medical staff needs to check sputum accumulation in patient's respiratory tract by lung sounds auscultation at any time, and it is the big burden for the staff. This paper aims to develop a system which notifies appropriate timing for the tracheal suction for the medical staff by analyzing lung sounds of the patients. We present a novel framework about automatic sputum detection from lung sounds. We proposed the sparse representation of audio features to realize robust detection in real environment. We showed the effectiveness of our proposed method for three patients in an ICU of Gifu University Hospital, where the recorded lung sounds included electronic beeps, human voices, and other various noises. © 2012 ICPR Org Committee.

Musical instrument identification is one of the major topics in music signal processing. In this paper, we propose a musical instrument identification method based on sparse representation for polyphonic sounds. Such the identification has been still categorized into challenging tasks, since it needs high-performance signal processing techniques. The proposed scheme can be applied without any signal processing such as source separation. Sample feature vectors for various musical instruments are used for the base matrix of sparse representation. We conducted two experiments to evaluate the proposed method. First, the musical instrument identification is tested for monophonic sounds using five musical instruments. The average accuracy of 91.9% was obtained and it shows the effectiveness of the proposed method. Second, musical instrument composition of polyphonic sounds is examined, which contain two instruments. It is found that the estimated weight vector by sparse representation indicates the mixture ratio of two instruments. © 2012 APSIPA.

In this paper, we propose to use noise reduction technology on both speech signal and visual signal by using exemplar-based sparse representation features for audio-visual speech recognition. First, we introduce sparse representation classification technology and describe how to utilize the sparse imputation to reduce noise not only for audio signal but also for visual signal. We utilize a normalization method to improve the accuracy of the sparse representation classification, and propose a method to reduce the error rate of visual signal when using the normalization method. We show the effectiveness of our proposed noise reduction method and that the audio features achieved up to 88.63% accuracy at -5dB, a 6.24% absolute improvement is achieved over the additive noise reduction method, and the visual features achieved 27.24% absolute improvement at gamma noise. © 2012 APSIPA.

In this paper, we introduce a bimodal speech recognition corpus in real environments. In recent years, speech recognition technology has been used in noisy conditions. Therefore, it becomes necessary to achieve higher recognition accuracy in real environments. As one of the solutions, bimodal speech recognition using audio and non-audio information is getting studied. However, there are few databases which can be used to evaluate the bimodal speech recognition in real environments. In this paper, we introduce CENSREC-2-AV we have been working to built, as a new bimodal speech recognition corpus. CENSREC-2-AV is one of the databases of the CEN-SREC project; we provided a similar corpus CENSREC-1-AV as a database for bimodal speech recognition for additive noises. In these corpora, there are speech data and lip images. Researchers can evaluate a bimodal speech recognition method built using CENSREC-1-AV which consists of clean data, in real environments by using CENSREC-2-AV. © 2012 IEEE.

This paper proposes a novel discriminative feature extraction method. The method consists of two stages; in the first stage, a classifier is built for each class, which categorizes an input vector into a certain class or not. From all the parameters of the classifiers, a first transformation can be formed. In the second stage, another transformation that generates a feature vector is subsequently obtained to reduce the dimension and enhance recognition ability. These transformations are computed applying genetic algorithm. In order to evaluate the performance of the proposed feature, speech recognition experiments were conducted. Results in clean training condition shows that GIF greatly improves recognition accuracy compared to conventional MFCC in noisy environments. Multi-condition results also clarifies that out proposed scheme is robust against differences of conditions.

In this paper, a multi-stream-based model adaptation method is proposed for speech recognition in noisy or real environments. The proposed scheme comes from our experience about audio-visual model adaptation. At first, an acoustic feature vector is divided into several vectors (e. g. static, first-order and second-order dynamic vectors), namely streams. While adaptation, a stream performing relatively high recognition performance is updated for the stream only. Alternatively, a stream having less recognition power is adapted using all the streams that are superior to the stream. In order to evaluate the proposed technique, recognition experiments were conducted using every streams, and then adaptation experiments were also investigated for various types of combination of streams.

In order to make voice conversion (VC) robust to noise, we propose VC using GA-based informative feature (GIF), by adding an extraction process of GIF to a conventional VC. GIF is proposed as a feature that can be applied not only in pattern recognition but also in relative tasks. In speech recognition, furthermore, GIF could improve recognition accuracy in noise environment. We evaluated the performances of VC using spectral segmental features (conventional method) and GIF, respectively. Objective experimental result indicates that in noise environments, the proposed method was better than the conventional method. Subjective experiment was also conducted to compare the performances. These results show that application of GIF to VC was effective.

In this paper, we propose a general and discriminative feature "GIF" (GA-based Informative Feature), and apply the feature to lipreading (visual speech recognition). The feature extraction method consists of two transforms, that convert an input vector to GIF for recognition. The transforms can be computed using training data and Genetic Algorithm (GA). For lipreading, we extract a fundamental feature as an input vector from an image; the vector consists of intensity values at all the pixels in an input lip image, which are enumerated from left-top to right-bottom. Recognition experiments of continuous digit utterances were conducted using an audio-visual corpus including more than 268,000 lip images. The recognition results show that the GIF-based method is better than the baseline method using eigenlip features.

This paper investigates audio-visual interaction, i.e. inter-modal influences, in linear-regressive model adaptation for multi-modal speech recognition. In the multi-modal adaptation, inter-modal information may contribute the performance of speech recognition. Thus the influence and advantage of intermodal elements should be examined. Experiments were conducted to evaluate several transformation matrices including or excluding inter-modal and intra-modal elements, using noisy data in an audio-visual corpus. From the experimental results, the importance of effective use of audio-visual interaction is clarified.

This paper introduces our recent effort to develop a Service-Operation Estimation (SOE) system using speech and multi-sensored data as well as other acquired data. In SOE, it is essential to analyze employees' data in order to increase the productivity in many service industries. Speech processing techniques, such as voice activity detection and keyword spotting recognition, help the analysis and enhance the precision of the results
the beginning and end times of speech region are used to detect work events, and recognized keywords are used to conduct work estimation. In our system all the results are visualized in a 3D model, and it makes employers and employees help their operations. © 2011 IEEE.

This paper studies usage of both keywords and captions in one scene for video content. Captions show the spoken content and are renewed in a sentence unit. A method is proposed to extract keywords automatically from transcribed texts. The method estimates topic boundary, extracts keywords by Latent Dirichlet Allocation (LDA) and presents them in speech balloon captioning system. The proposed method is evaluated by experiments from the viewpoint of easy to view and helpfulness to understand the video content. Adding keywords and captions obtained favorable scores by subjective assessments.

This paper proposes a Template-based Spectral Estimation (TSE) method for noise reduction of microphone array processing aiming at speech recognition enhancement. In the proposed method, a noise template in a complex plane is calculated for each frequency bin using non-speech audio signals observed at microphones. Then for every noise-overlapped speech signals, a speech signal can be reformed by applying the template and the gradient descent method. Experiments were conducted to evaluate not only performance of noise reduction but also improvement of speech recognition. Then NRR 16.7dB improvement was achieved by combining TSE and Spectral Subtraction (SS) methods. For speech recognition, 44% relative recognition error reduction was obtained comparing with the conventional SS method.

This paper proposes a novel speech recognition method combining Audio-Visual Voice Activity Detection (AVVAD) and Audio-Visual Automatic Speech Recognition (AVASR). AVASR has been developed to enhance the robustness of ASR in noisy environments, using visual information in addition to acoustic features. Similarly, AVVAD increases the precision of VAD in noisy conditions, which detects presence of speech from an audio signal. In our approach, AVVAD is conducted as a preprocessing followed by an AVASR system, making a significantly robust speech recognizer. To evaluate the proposed system, recognition experiments were conducted using noisy audio-visual data, testing several AVVAD approaches. Then it is found that the proposed AVASR system using the model-free feature-fusion AVVAD method outperforms not only non-VAD audio-only ASR but also conventional AVASR.

The purpose of our research is to summarize the medical care video by treatment detection and situation classification based on the positional relation between patient and medical staff by utilizing the feature of the medical care video. As the first step, we discussed the automated detection of the treatment in video of emergency medical care only by position information and motion information and proposed the simple way to detect the cardiac massage at low cost.

This paper reports a metadata generation system as well as an automatic video edit system. The metadata are information described about the other data. In the audio metadata. generation system, speech recognition using general language model (LM) and specialized LM is performed to input speech in order to obtain segment (event group) and audio metadata (event information) respectively. In the video edit system, visual metadata obtained by image recognition and audio metadata are combined into audio-visual metadata. Subsequently, multiple videos are edited to one video using the audio-visual metadata. Experiments were conducted to evaluate event detection of the systems using medical education contents, ACLS and BLS. The audio metadata system achieved about a 78% event detection correctness. In the edit system, an 87% event correctness was obtained by audio-visual metadata, and the survey proved that the edited video is appropriate and useful.