Ensemble Acoustic Modeling for CD-DNN-HMM Using Random Forests of Phonetic Decision Trees

Zhao, Tuo; Zhao, Yunxin; Chen, Xin

doi:10.1007/s11265-015-1001-9

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Even with hybrid methods, the estimation of the acoustic model is always done in the same manner. Generally, in these methods, the results of the first algorithm are used to improve the results of the other algorithm as in [20,21].…”

Section: Proposed Speech Recognition Systemmentioning

confidence: 99%

Recognition of isolated digits using DNN–HMM and harmonic noise model

Guerid¹,

Houacine

2019

IET signal process.

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Speech recognition is an area that is constantly developing. In this study, the authors present a new system of speech recognition applied to the Arabic language. The system proposed here is based on the harmonic plus noise model (HNM). This model is rather used in speech synthesis tasks and is known for providing excellent speech production quality. Thus, their contribution lies in replacing the conventional mel-frequency cepstrum coefficients (MFCC) parameters with a set of acoustic parameters, extracted through the HNM estimation process. The HNM model allows development of a more adapted processing by distinguishing voiced and unvoiced speech frames and by characterising the harmonic property of speech. As common, their system consists of both training and recognition phases. Deep neural networks and hidden Markov models (DNN-HMM) are used for modelling the voiced frames corresponding to the harmonic part. The DNN model is estimated with static and dynamic parameters. Moreover, the unvoiced frames, which represent the noise part of the HNM, are clustered with an HMM model. The spoken Arabic digits are used to measure the performance of the proposed recognition system and a comparison with the MFCC-based approach is performed.

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Section: Proposed Speech Recognition Systemmentioning

confidence: 99%

Recognition of isolated digits using DNN–HMM and harmonic noise model

Guerid¹,

Houacine

2019

IET signal process.

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“…Later, a pretrained DNN-HMM is considered on acoustic modeling with varying depths of networks [17]. Further, the Deep neural network is used for speech recognition for large vocabulary speech tasks [18].…”

Section: Introductionmentioning

confidence: 99%

Convolution neural networks with hybrid feature extraction methods for classification of voice sound signals

Rashmi¹,

Singh²

2023

World J. Adv. Eng. Technol. Sci.

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The convolutional neural networks (CNNs) lead in the domain of Sound Recognition due to its flexibility and ability with different adjusting parameters. The recognition of spoken English Alphabets by different people with deep learning techniques attracted the research community. In this paper, we are exploring the use of convolutional neural network (CNN), a deep learner that can automatically learn features directly from the dataset while training for the classification of sounds signals of English alphabets. In this proposed work, we consider two CNN architectures. In first architecture, we propose MFCC based features for pretrained two convolutional layer CNN architecture. In the second architecture, we propose a hybrid feature extraction method to train a block-based CNN architecture. The proposed systems consist of two components namely hybrid feature extraction and CNN classifier. The five auditory features log-Mel spectrogram (LM), MFCC, chroma, spectral contrast and Tonnetz features are extracted and then LM & MFCC are combined as one feature set. LM, MFCC, and CST features are aggregated as another for training to the proposed two CNNs, respectively. The different sound samples of English alphabets are collected from different people of different age groups. The feature sets collected from the hybrid feature extraction methods are presented to both the proposed CNNs and the experimental results are collected. The experimental results indicate that the taxonomic accuracy of the proposed architectures can surpass the existing methods of CNNs with single feature extraction methods. The proposed second architecture performs more effectively over the proposed first CNN architecture.

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“…It has been recently found that deep learning [17,18] has achieved remarkable success in many speech processing fields with its excellent learning performance. The representative technology is DNN-HMM hybrid structure [19,20], replacing the conventional acoustic modeling based on GMM and HMM. In single-channel speech separation, a method based on DNNs [21,22] has been proposed to separate the target speaker from the mixed speech.…”

Section: Introductionmentioning

confidence: 99%

An Improved Unsupervised Single‐Channel Speech Separation Algorithm for Processing Speech Sensor Signals

Jiang

Lin

et al. 2021

Wireless Communications and Mobile Computing

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As network supporting devices and sensors in the Internet of Things are leaping forward, countless real-world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low-noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single-channel signal, many technical challenges are imposed on achieving single-channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non-Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform-based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.

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Ensemble Acoustic Modeling for CD-DNN-HMM Using Random Forests of Phonetic Decision Trees

Cited by 5 publications

References 19 publications

Recognition of isolated digits using DNN–HMM and harmonic noise model

Recognition of isolated digits using DNN–HMM and harmonic noise model

Convolution neural networks with hybrid feature extraction methods for classification of voice sound signals

An Improved Unsupervised Single‐Channel Speech Separation Algorithm for Processing Speech Sensor Signals

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