Optimizing feature extraction techniques constituting phone based modelling on connected words for Punjabi automatic speech recognition

Kaur, Arshpreet; Singh, Amitoj

doi:10.1109/icacci.2016.7732362

Cited by 11 publications

(1 citation statement)

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“…94% accuracy has been reported. Kaur and Singh [146] compared three feature extraction techniques, such as PNCC, PLP, and MFCC. The system was trained using HMM.…”

Section: Punjabimentioning

confidence: 99%

Computational intelligence in processing of speech acoustics: a survey

Singh

Kaur

Kukreja

et al. 2022

Complex Intell. Syst.

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Speech recognition of a language is a key area in the field of pattern recognition. This paper presents a comprehensive survey on the speech recognition techniques for non-Indian and Indian languages, and compiled some of the computational models used for processing speech acoustics. An immense number of frameworks are available for speech processing and recognition for languages persisting around the globe. However, a limited number of automatic speech recognition systems are available for commercial use. The gap between the languages being spoken around the globe and the technical support available to these languages are very few. This paper examined major challenges for speech recognition for different languages. Analysis of the literature shows that lack of standard databases availability of minority languages hinder the research recognition research across the globe. When compared with non-Indian languages, the research on speech recognition of Indian languages (except Hindi) has not achieved the expected milestone yet. Combination of MFCC and DNN–HMM classifier is most commonly used system for developing ASR minority languages, whereas in some of the majority languages, researchers are using much advance algorithms of DNN. It has also been observed that the research in this field is quite thin and still more research needs to be carried out, particularly in the case of minority languages.

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“…94% accuracy has been reported. Kaur and Singh [146] compared three feature extraction techniques, such as PNCC, PLP, and MFCC. The system was trained using HMM.…”

Section: Punjabimentioning

confidence: 99%