Discriminatively trained continuous Hindi speech recognition system using interpolated recurrent neural network language modeling

Dua, Mohit; Aggarwal, Rajesh; Biswas, Mantosh

doi:10.1007/s00521-018-3499-9

Cited by 26 publications

(8 citation statements)

References 38 publications

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“…They achieved a relative improvement of 85.45 under clean and 82.95 under noisy conditions Kadyan et al [ 46 ] Punjabi adult corpora comprising of isolated and phonetically rich sentences MFCC coupled bottleneck features based on Tandem-NN acoustic modeling In this paper, the authors have processed context-independent input speech signal information through utilization of bottleneck characteristics. Further noisy data have been handled and experimental results revealed that under clean and noisy settings a Tandem-NN system achieved a RI of 13.53% as compared to the Baseline system Dua et al [ 47 ] Hindi continuous sentences speech corpora and noise augmented dataset Use of noise-resistant integrated features and an improved HMM model for the development of discriminatively trained speech recognition system The suggested study has examined that with MF-PLP and MF-GFCC alone or integrated feature vectors results into large performance improvement Kumar and Aggarwal [ 48 ] Two low-resource Indo-Aryan family languages including Hindi and Marathi Integrated features vector with RNN being employed on Hindi ASR system utilizing MLLR and constrained-MLLR) The researcher experimented 256 Gaussian mixtures corresponding to every HMM state using discriminatively trained method of MMI and MPE. The experiments showcased that the discriminative training has been improved in comparison to baseline system by 3% Bawa et al [ 1 ] Gender-based selection under mismatched conditions MFCC; GFCC-based DNN–HMM modeling The study attempts to create Punjabi Children ASR in mismatched parameters via noise-robust techniques such as the MFCC or GFCC.…”

Section: Resultsmentioning

confidence: 99%

Developing sequentially trained robust Punjabi speech recognition system under matched and mismatched conditions

Bawa

Kadyan

Tripathy³

et al. 2022

Complex Intell. Syst.

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Development of a native language robust ASR framework is very challenging as well as an active area of research. Although an urge for investigation of effective front-end as well as back-end approaches are required for tackling environment differences, large training complexity and inter-speaker variability in achieving success of a recognition system. In this paper, four front-end approaches: mel-frequency cepstral coefficients (MFCC), Gammatone frequency cepstral coefficients (GFCC), relative spectral-perceptual linear prediction (RASTA-PLP) and power-normalized cepstral coefficients (PNCC) have been investigated to generate unique and robust feature vectors at different SNR values. Furthermore, to handle the large training data complexity, parameter optimization has been performed with sequence-discriminative training techniques: maximum mutual information (MMI), minimum phone error (MPE), boosted-MMI (bMMI), and state-level minimum Bayes risk (sMBR). It has been demonstrated by selection of an optimal value of parameters using lattice generation, and adjustments of learning rates. In proposed framework, four different systems have been tested by analyzing various feature extraction approaches (with or without speaker normalization through Vocal Tract Length Normalization (VTLN) approach in test set) and classification strategy on with or without artificial extension of train dataset. To compare each system performance, true matched (adult train and test—S1, child train and test—S2) and mismatched (adult train and child test—S3, adult + child train and child test—S4) systems on large adult and very small Punjabi clean speech corpus have been demonstrated. Consequently, gender-based in-domain data augmented is used to moderate acoustic and phonetic variations throughout adult and children’s speech under mismatched conditions. The experiment result shows that an effective framework developed on PNCC + VTLN front-end approach using TDNN-sMBR-based model through parameter optimization technique yields a relative improvement (RI) of 40.18%, 47.51%, and 49.87% in matched, mismatched and gender-based in-domain augmented system under typical clean and noisy conditions, respectively.

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Section: Resultsmentioning

confidence: 99%

Developing sequentially trained robust Punjabi speech recognition system under matched and mismatched conditions

Bawa

Kadyan

Tripathy³

et al. 2022

Complex Intell. Syst.

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“…Mohit Dua, R et al [12] construct and evaluate a continuous Hindi language speech recognition system that has been discriminatively trained. In order to train the automatic speech recognition (ASR) system, the system utilises maximum mutual information and Algorithm:…”

Section: Related Workmentioning

confidence: 99%

Spoken Language Recognization Based on Features and Classification Methods

Bam

Degadwala²,

Upadhyay

et al. 2022

IJSRCSEIT

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In Western countries, speech-recognition applications are accepted. In East Asia, it isn't as common. The complexity of the language might be one of the main reasons for this latency. Furthermore, multilingual nations such as India must be considered in order to achieve language recognition (words and phrases) utilizing speech signals. In the last decade, experts have been clamoring for more study on speech. In the initial part of the pre-processing step, a pitch and audio feature extraction technique were used, followed by a deep learning classification method, to properly identify the spoken language. Various feature extraction approaches will be discussed in this review, along with their advantages and disadvantages. Purpose of this research is to Learn transfer learning approaches like Alexnet, VGGNet, and ResNet & CNN etc. using CNN model we got best accuracy for Language Recognition.

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“…Most popular ASR systems are based on statistical-based acoustic modeling. In the past few years, the discriminative technique gets more attention as it further optimizes the HMM parameters to achieve high accuracy [14,37]. In conventional GMM-HMM based acoustic modeling, HMM parameters are estimated via MLE technique [18].…”

Section: Discriminative Techniquesmentioning

confidence: 99%

Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling

Kumar

Aggarwal

2020

Journal of Intelligent Systems

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This paper implements the continuous Hindi Automatic Speech Recognition (ASR) system using the proposed integrated features vector with Recurrent Neural Network (RNN) based Language Modeling (LM). The proposed system also implements the speaker adaptation using Maximum-Likelihood Linear Regression (MLLR) and Constrained Maximum likelihood Linear Regression (C-MLLR). This system is discriminatively trained by Maximum Mutual Information (MMI) and Minimum Phone Error (MPE) techniques with 256 Gaussian mixture per Hidden Markov Model(HMM) state. The training of the baseline system has been done using a phonetically rich Hindi dataset. The results show that discriminative training enhances the baseline system performance by up to 3%. Further improvement of ~7% has been recorded by applying RNN LM. The proposed Hindi ASR system shows significant performance improvement over other current state-of-the-art techniques.

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Discriminatively trained continuous Hindi speech recognition system using interpolated recurrent neural network language modeling

Cited by 26 publications

References 38 publications

Developing sequentially trained robust Punjabi speech recognition system under matched and mismatched conditions

Developing sequentially trained robust Punjabi speech recognition system under matched and mismatched conditions

Spoken Language Recognization Based on Features and Classification Methods

Discriminatively trained continuous Hindi speech recognition using integrated acoustic features and recurrent neural network language modeling

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