Development of Robust Automatic Speech Recognition System for Children's using Kaldi Toolkit

Bhardwaj, Vivek; Bala, Sashi; Kadyan, Virender; Kukreja, Vinay

doi:10.1109/icirca48905.2020.9182941

Cited by 41 publications

(2 citation statements)

References 12 publications

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“…Research in [28] presented a Punjabi language speech recognition system for children's speech, which was developed using SGMM-HMM. It showed that using SGMM for acoustic modeling of small vocabulary datasets resulted in significant improvements for those small data sets.…”

Section: Development In Asr Modelsmentioning

confidence: 99%

A Novel Human-Vehicle Interaction Assistive Device for Arab Drivers Using Speech Recognition

2022

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About one-quarter of all car collisions in the United States are caused by distracted driving, and this ratio is expected to rise. As vehicles are equipped with more elaborate and complex technology, human-vehicle interaction via dashboard displays and controls will become more complex and distracting. Human-vehicle interaction via voice-based technology offers a less distracting alternative. In this study we aim to develop a voice-based car assistant, with a focus on Arabic language speech recognition. We prepare a new 4000-word domain-specific lexicon to comprehensively support driver-vehicle interactions, and we create corresponding text and speech corpora. Then we extract acoustic feature vectors and use various acoustic models to support speech recognition. The language model is created using an n-gram model. Then acoustic and language models, and the lexicon are combined to generate a decoding graph. The text corpus consists of 6110 elements, including words, phrases, and sentences. The speech corpus has more than 60000 recordings (almost 50 hours). For the decoding of noise-free audio, a Deep Neural Network + Hidden Markov Model provided 94.832% accuracy, a Subspace Gaussian Mixture Model + Hidden Markov Model provided 94.2% accuracy, and the best Gaussian Mixture Model + Hidden Markov Model provided 94.13% accuracy. For the decoding of noisy audio, a Deep Neural Network + Hidden Markov Model provided 93.316% accuracy, a Subspace Gaussian Mixture Model + Hidden Markov Model provided 92.62% accuracy, and the best Gaussian Mixture Model + Hidden Markov Model provided 91.82% accuracy. A usability study was conducted on the system with 10 participants. Almost all of the results of that study showed usability ratings of greater than 4.0 out of 5.0. These usability ratings indicate that the proposed system was seen by the participants as important, and useful for reducing driver distraction.

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Section: Development In Asr Modelsmentioning

confidence: 99%

A Novel Human-Vehicle Interaction Assistive Device for Arab Drivers Using Speech Recognition

2022

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“…Mathematical expression in Eq. ( 9) is used by the speech recognizer to find more appropriate words [13,28,29].…”

Section: Language Model (Lm)mentioning

confidence: 99%

Usage of Prosody Modification and Acoustic Adaptation for Robust Automatic Speech Recognition (ASR) System

Bhardwaj¹,

Kukreja²,

Singh³

2021

RIA

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Most of the automatic speech recognition (ASR) systems are trained using adult speech due to the less availability of the children's speech dataset. The speech recognition rate of such systems is very less when tested using the children's speech, due to the presence of the inter-speaker acoustic variabilities between the adults and children's speech. These inter-speaker acoustic variabilities are mainly because of the higher pitch and lower speaking rate of the children. Thus, the main objective of the research work is to increase the speech recognition rate of the Punjabi-ASR system by reducing these inter-speaker acoustic variabilities with the help of prosody modification and speaker adaptive training. The pitch period and duration (speaking rate) of the speech signal can be altered with prosody modification without influencing the naturalness, message of the signal and helps to overcome the acoustic variations present in the adult's and children's speech. The developed Punjabi-ASR system is trained with the help of adult speech and prosody-modified adult speech. This prosody modified speech overcomes the massive need for children's speech for training the ASR system and improves the recognition rate. Results show that prosody modification and speaker adaptive training helps to minimize the word error rate (WER) of the Punjabi-ASR system to 8.79% when tested using children's speech.

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A Comprehensive Analysis on Kaldi-Based Speech Recognition for Low Resource Indian Languages

Suhani,

Dev,

Bansal

2024

Communications in Computer and Information Science

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Development of Robust Automatic Speech Recognition System for Children's using Kaldi Toolkit

Cited by 41 publications

References 12 publications

A Novel Human-Vehicle Interaction Assistive Device for Arab Drivers Using Speech Recognition

A Novel Human-Vehicle Interaction Assistive Device for Arab Drivers Using Speech Recognition

Usage of Prosody Modification and Acoustic Adaptation for Robust Automatic Speech Recognition (ASR) System

A Comprehensive Analysis on Kaldi-Based Speech Recognition for Low Resource Indian Languages

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