Acoustic modeling problem for automatic speech recognition system: advances and refinements (Part II)

Aggarwal, Rajesh; Dave, Mayank

doi:10.1007/s10772-011-9106-4

Cited by 15 publications

(5 citation statements)

References 74 publications

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“…Features such as delta coefficient, segmental statistics and modulation spectrum have been developed which can deal with phenomena of straddling. Aggarwal and Dave [12] have reviewed the variety of modifications and extensions adopted for the HMM based acoustic models in the form of refinements such as variable duration models, discriminative techniques, connectionist approach (HMM+ANN) to overcome the limitations of traditional HMM and advancements such as margin based methods, wavelets and dual stream approach. Ostendorf et al [13] also came up with segmental models to overcome this weakness of HMM.…”

Section: Acoustic Modelmentioning

confidence: 99%

Literature Review on Automatic Speech Recognition

Ghai¹,

Singh²

2012

IJCA

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Automatic speech recognition, which was considered to be a concept of science fiction and which has been hit by number of performance degrading factors, is now an important part of information and communication technology. Improvements in the fundamental approaches and development of new approaches by researchers have lead to the advancement of ASRs which were just responding to a set of sounds to sophisticated ASRs which responds to fluently spoken natural language. Using artificial neural networks (ANNs), mathematical models of the low-level circuits in the human brain, to improve speech-recognition performance, through a model known as the ANN-Hidden Markov Model (ANN-HMM) have shown promise for large-vocabulary speech recognition systems. Achieving higher Recognition accuracy, low Word error rate, developing speech corpus depending upon the nature of language and addressing the issues of sources of variability through approaches like Missing Data Techniques & Convolutive Non-Negative Matrix Factorization, are the major considerations for developing an efficient ASR. In this paper, an effort has been made to highlight the progress made so far for ASRs of different languages and the technological perspective of automatic speech recognition in countries like China, Russian,

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Section: Acoustic Modelmentioning

confidence: 99%

Literature Review on Automatic Speech Recognition

Ghai¹,

Singh²

2012

IJCA

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“…However, ANN-based approaches require large amounts of data to produce accurate results. Therefore, to develop a speaker-independent and high-quality performance (i.e., low word error rate) ASR, an extensive collection of speech samples must be gathered from various speakers along with the transcriptions of those speeches [10].…”

Section: Introductionmentioning

confidence: 99%

Building a Speech and Text Corpus of Turkish: Large Corpus Collection with Initial Speech Recognition Results

Polat

Oyucu

2020

Symmetry

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To build automatic speech recognition (ASR) systems with a low word error rate (WER), a large speech and text corpus is needed. Corpus preparation is the first step required for developing an ASR system for a language with few argument speech documents available. Turkish is a language with limited resources for ASR. Therefore, development of a symmetric Turkish transcribed speech corpus according to the high resources languages corpora is crucial for improving and promoting Turkish speech recognition activities. In this study, we constructed a viable alternative to classical transcribed corpus preparation techniques for collecting Turkish speech data. In the presented approach, three different methods were used. In the first step, subtitles, which are mainly supplied for people with hearing difficulties, were used as transcriptions for the speech utterances obtained from movies. In the second step, data were collected via a mobile application. In the third step, a transfer learning approach to the Grand National Assembly of Turkey session records (videotext) was used. We also provide the initial speech recognition results of artificial neural network and Gaussian mixture-model-based acoustic models for Turkish. For training models, the newly collected corpus and other existing corpora published by the Linguistic Data Consortium were used. In light of the test results of the other existing corpora, the current study showed the relative contribution of corpus variability in a symmetric speech recognition task. The decrease in WER after including the new corpus was more evident with increased verified data size, compensating for the status of Turkish as a low resource language. For further studies, the importance of the corpus and language model in the success of the Turkish ASR system is shown.

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“…It requires an understanding of the acoustic-phonetic knowledge, microphone and environment variability issues, gender, and dialectal differences. Further, for determining the connection between linguistic units and acoustic observation, rigorous training is required [11]. AM is also directly linked to pronunciation modeling, variability modeling related to speaker, environment, and contexts also [12].…”

Section: Introductionmentioning

confidence: 99%

Acoustic Modeling in Speech Recognition: A Systematic Review

Bhatt¹,

Jain²,

Dev³

2020

IJACSA

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The paper presents a systematic review of acoustic modeling (AM) techniques in speech recognition(SR). Acoustic modeling establishes a relationship between acoustic information and language construct in SR. Over the past decades, researchers presented studies addressing specific concerns in AM. However, all previous research works lack a systematic and comprehensive review of acoustic modeling issues. A systematic review is introduced to understand the acoustic modeling issues in speech recognition. This paper provides an extensive and comprehensive inspection of various researches that have been performed since 1984. The extensive investigation and analysis into AM was performed by getting the relevant data from 73 research works chose after the screening process between the years from 1984 to 2020. The systematic review process was divided into different parts to investigate acoustic modeling issues. Main issues in acoustic modeling such as feature extraction techniques, acoustic modeling units, speech corpora, classification methods, different tools used, language issues applied, and evaluation parameters were investigated. This study helps the reader to understand various acoustic modeling issues with comprehensive details. The research outcomes presented in this study depict research trends and shed light on new research topics in AM. The result of this review can be used to build a better speech recognition system by choosing a suitable acoustic modeling construct in SR.

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Acoustic modeling problem for automatic speech recognition system: advances and refinements (Part II)

Cited by 15 publications

References 74 publications

Literature Review on Automatic Speech Recognition

Literature Review on Automatic Speech Recognition

Building a Speech and Text Corpus of Turkish: Large Corpus Collection with Initial Speech Recognition Results

Acoustic Modeling in Speech Recognition: A Systematic Review

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