A Review on Acoustic Phonetic Approach for Marathi Speech Recognition

Pawar, V. P.

doi:10.5120/9523-3934

Cited by 7 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Acoustic phonetic approach comprises mainly of two steps: • The first is speech spectral analysis, which describes the various phonetic units' overall acoustic qualities. The speech is then segmented and labeled, resulting in a lattice of phonemes characterization of speech [53]. • Next, a string of words or a legitimate word is determined [6].…”

Section: Acoustic Phonetic Approachmentioning

confidence: 99%

Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

Basak¹,

Agrawal²,

Jena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Speech recognition systems have become a unique human-computer interaction (HCI) family. Speech is one of the most naturally developed human abilities; speech signal processing opens up a transparent and hand-free computation experience. This paper aims to present a retrospective yet modern approach to the world of speech recognition systems. The development journey of ASR (Automatic Speech Recognition) has seen quite a few milestones and breakthrough technologies that have been highlighted in this paper. A step-by-step rundown of the fundamental stages in developing speech recognition systems has been presented, along with a brief discussion of various modern-day developments and applications in this domain. This review paper aims to summarize and provide a beginning point for those starting in the vast field of speech signal processing. Since speech recognition has a vast potential in various industries like telecommunication, emotion recognition, healthcare, etc., this review would be helpful to researchers who aim at exploring more applications that society can quickly adopt in future years of evolution. KEYWORDSSpeech recognition; automatic speech recognition (ASR); mel-frequency cepstral coefficients (MFCC); hidden Markov model (HMM); artificial neural network (ANN)

show abstract

Section: Acoustic Phonetic Approachmentioning

confidence: 99%

Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

Basak¹,

Agrawal²,

Jena³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Acoustic Phonetic Approach: Acoustics and Phonetics [7] [8] are the study of different sounds and phonemes of the language respectively. Acoustic phonetic approach of speech recognition relies on the postulates that every spoken language will have finite, definite and distinctive phonetic units whose properties are determined either by the time-domain signal or the spectrum of the signal.…”

Section: Fig2 Classification Of Speech Recognition Approachesmentioning

confidence: 99%

An Extensive Review of Feature Extraction Techniques, Challenges and Trends in Automatic Speech Recognition

Kanabur¹,

Harakannanavar²,

Torse³

2019

IJIGSP

View full text Add to dashboard Cite

Speech is the natural mode of communication between humans. Human-to-machine interaction is gaining importance in the past few decades which demands the machine to be able to analyze, respond and perform tasks at the same speed as performed by human. This task is achieved by Automatic Speech Recognition (ASR) system which is typically a speech-to-text converter. In order to recognize the areas of further research in ASR, one must be aware of the current approaches, challenges faced by each and issues that needs to be addressed. Therefore, in this paper human speech production mechanism is discussed. The various speech recognition techniques and models are addressed in detail. The performance parameters that measure the accuracy of the system in recognizing the speech signal are described.

show abstract

“…Marathi [8,10,12,13,15,16,18,20,26,63,78] Indo-Aryan Gujarati [8, 16-21, 64, 78] Indo-Aryan Hindi [9, 15, 22-24, 26, 27, 27, 60, 61, 70, 71] Indo-Aryan Bengali [6, 9, 26, 28-30, 32, 62, 71] Indo-Aryan Assamese [21,[33][34][35][36][37] the other channel was a head set microphone, where data were recorded at 48 kHz sampling frequency and 16 bits/sample resolution. It was transcribed using the IPA symbols.…”

Section: Languagementioning

confidence: 99%

Spoken Indian language identification: a review of features and databases

Bakshi

Kopparapu

2018

Sādhanā

View full text Add to dashboard Cite

Spoken language is one of the distinctive characteristics of the human race. Spoken language processing is a branch of computer science that plays an important role in human-computer interaction (HCI), which has made remarkable advancement in the last two decades. This paper reviews and summarizes the acoustic, phonetic and prosody features that have been used for spoken language identification specifically for Indian languages. In addition, we also review the speech databases, which are already available for Indian languages and can be used for the purposes of spoken language identification.

show abstract

A Review on Acoustic Phonetic Approach for Marathi Speech Recognition

Cited by 7 publications

References 7 publications

Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems

An Extensive Review of Feature Extraction Techniques, Challenges and Trends in Automatic Speech Recognition

Spoken Indian language identification: a review of features and databases

Contact Info

Product

Resources

About